TECHNICAL SKILLS:

Big Data Ecosystem/Programming Languages: Hadoop 2.7, HBASE. HDFS, Flume, Scoop, Oozie, Yarn Release 2.0, Mesos, Storm, Kafka, Spark 1.6.1(SQL/Core/GraphX/MLLib/DataFrames/RDDs), Neo4J, MongoDB, Cassandra 2.0, Riak, Talend Open Studio 5.6, Pig, Hive, lambda architecture - batch versus near real-time, Aerospike RDBMS ETL, Cassandra 2.0, Riak, MongoDB, R, Java 7/8/Eclipse - Helios/Juno/Mars, Python 2.6/3.3, AeroSpike; Avro/Parquet/JSON data formats, Kerberos 5, Java cryptography (AES-256 bit), Java 8 and RestFul API; Tachyon 0.8.0, Apache Zeppelin incubator 0.5.5; Flink 0.10.0 (Hadoop 2.7/Scala 2.11 IDE), compression strategies - Gzip, LZO, Snappy, serialization - Java/Kyro, akka/Actor, Zeppelin notebooks: CrateDB/Grafana; Kavlev Data Modeling Tool(Cassandra 3.0), DataDog, Zookeeper/Kafka tuning

Cloud Technologies (AWS):EC2/EC2 Container Service(Docker), Elastic Beanstalk, Lambda(event driven architecture); Storage - S3, Elastic File System, Direct Connect, Route 53, CloudWatch(autoscaling), Cloud Formation, CloudTrail, Config, OpWorks, Identity and Access Management, Inspector, EMR, Kinesis, Machine Learning algorithms, API Gateway, AppStream, enterprise AWS cloud design patterns, AWS (IoT); RedShift, Storage model analysis - hourly, reserved, spot-pricing, autoscaling, Snowball, Redshift; EKS, Glue, AutoScaling

Dev Ops: Splunk, Nagios, Ganglia, JVM Tuning, MemCache, Tachymon, Project Tungsten, scripting - bash, awk, sed, shells, Linux kernel / io device modifications, Puppet/Chef/Shellshock, Ansible Tower, Linux - Red Hat RHE 6, CentOS, Ubuntu 12.3, Fedora 15/16/17; RANCID, Cacti(Graph), lldpd, IPerf, MultiHost SSHWrapper, Jenkins, Hudson CI, Bluepill, Capistrano, Bcfg2, Supervisor, Graylog, runit, Squid, snort, system, netstat, iostat, vmstat, ltrace, strace, ftrace, perf, tcpdump, sar, man, Takapi, GitHub, ssh, Cygwin, WinDiff, putty, Maven, Ant, DTraceToolkit(Netflix), Selenium, Node.js, putty socket.io/WebSockets, Azul Technologies - Zing; Stackio/Stacki(large scale Linux cluster automated deployments)

PROFESSIONAL EXPERIENCE:

Lead Kafka AWS Architect

Confidential, Dover, New Hampshire

Responsibilities:

• Designed and architected on AWS Kafka 0.10 version on Kubernetes - complete with pods, nodes, used HELM and Tiller to deploy charts(multiple yaml) files to the Kebernetes cluster; set up the headless IP load
• Balancer for a common IP static address; resoled the DNS conflicts and “floating IP addresses”; reconfigured
• The JVM requirements to match the k8 cluster; used various kubectl commands to manage and configure
• The k8 pods and nodes; created the k8 services for Zookeeper (3)3; configured 2 autoscaling groups - Kafka and Zookeeper; utilized various github tools(Yahoo) to troubleshoot the Kafka/Kubernetes/AWS cluster; set up the Kafka Replicator across two separate AZ’s(availability zones); research various security models for in-flight encryption and stationary encryption; trsade-offs in increased CPU utilization; researched ino AVRO serialization between Kafka producers and consumers
• Taught a custom Kafka 3 day course to the client dv ops engineers on the operation, security,logging,
• performance tuning, best practices, customer rebalance algorithm, leader follower/topic allocation strategiesKafaTool usage, system/systemctl startup scripts, adding/deleting topics, partitions; Zookeeper command line processing; Kafka properties files and use for tuning and maintenance; how to set up robust benchmarks for consumers and producers; setting up custom Kafka installs in Apache and Confluence Open Source; analysis of the Kafka ZeroCopy algorithm for consumers; how to set up a custom partitioner instead of round robin allocation; how to activate JMX parameters for Kafka and Zookeeper; how to set up BASH scripts for log scanning across the entire Kafka cluster; monitoring size of data logs; set up Restful interfaces, Kafka Schema Registry, Kafka Connect to MySQL database for persistence layer
• Created the Kafka-Flink integration plan incorporating the Confluent Enterprise Kafka version 0.11 and the Data Artisan Streaming version of Flink; support for Kubernetes container technology; transformed the use case of
• 20,00,000 IoT devices emitting 2 messages per minute over a 24 by 7 time frame; devised the Kafka topics thru a custom partitioner, adding a salt to improve the randomness and dispersion of messages evenly across all partitions; devised the DR plan of using MirrorMaker between two cities between Dover, NH and Boston, Ma.; worked with site reliability engineers to us portions of NetFlix Chaos, a framework for injecting random errors into the Big Data framework; worked with site reliability engineers for a 360 degree view of the various logs distributed in the Big Data topology; established smart cronjobs under RHEL7 and a centralized reporting/and email functionality for Level 1 and 2 support; integrated the Flink Kafka Consumer for Flink job to listen on specific Kafka topics; designed Flink savepoints to replay with different versions of Flink applications - replayed the same stream of data across different application logic; tackled critical issues involving backpressure, adjusted the size of the memory buffers to handle changes in the velocity of the streaming data; Tested and implemented the Kafka consumer offset with the Flink map task; worked with the ElasticSearch architect to build out the Flink ElasticSearchSink Connector In Java; implemented the Flink checkpoint feature for at-least once delivery; modified the Kafka consumers to emit watermark(digital trac) to promote in-flight topic tracing and message accountability through special Java methods; designed and implemented the ElasticSearch Kafka Data Connector for Kafka Consumer; used Java profilers to discern hot spots in Java methods within Java packages
• Downloaded from Apache.org the latest stable version of Spark 2.2.1 binaries; overlaid the Hortonworks 2.4
• distribution of Hadoop/HDFS/HBase via Ambari; installed Java+, Scala 2.11; 75 nodes - 32 cores, 30 Tb diskseach disk 6 Tb 10000 rpm, 2Gb SSD for Linux OS(Red Hat 7.3 Enterprise edition), 10 Gb Ethernet backbone; master case(s) analysis, 600+Gb customer data of car accidents, ran police reports, demographic information from state DMV’s; utilized MLlib regression models (for 2/3 pass in memory algorithms across multiple Spark workers; leveraged DataFrame API for Tungsten memory management optimization instead of the classic JVM
• (young Eden); resolved the cross licensing issues with the numerical programming package Breeze for optimized
• Numerical processing; utilized the Power Iteration Clustering algorithm for finding logical groups based on age, sex, geography, type of car, length of driving for insurance coverage characteristics; downloaded Mesos from mirror to submit Spark batch jobs into Mesos 1.0.0; established and designed Spark test jobs for incremental performance testing(1 Mb, 10 Mb, 100 Mb, 1Gb, 10 Gb) on dev on dev and a Spark clusters, varying the number of cores and RAM available for each Spark worker; installed Datadog to monitor the Spark cluster across dev, a and prod environments monitoring the performance of the Spark job(s); refactored Spark batch jobs to mitigate excessive shuffling and heuristic salting of the keys to prevent overloading on certain Spark nodes - reduce
• CPU and I/O contention
• Led screening effort to pick best architects in the industry for building out the associate Big Data
• Solutions architect role - including Walt Disney, MasterCard and Coca-Cola; looked for candidates
• With deep knowledge in data streaming with Kafka and Flink; created a 50 point skills evaluation and
• Big Data use case and design scenario for evaluating candidate strengths and weaknesses
• Conceived and designed a 80 page Kafka playbook for operations and tuning high performance low
• latency messaging systems - tuning broker, producer and consumer nodes, Java gc()
• Created a 110 page cookbook on using DataDog and creating customer performance dashboards
• for Zookeeper; created a 100 page cookbook for creating Kafka performance dashboards
• Conceived and created 30 page architecture design document for integrating Kafka consumer to
• a Hadoop batch gateway via HortonWorks release 2.4 Ambari REST services; integrated Kafka consumer with SPENEGO/Kerberos 5 authentication services; custom document for Kafka - Hadoop data transfer and integration
• Installed the Confluent 3.2.1 Kafka broker and Operation Dashboard; ran and modified the performance
• Kafka load against DataDog Zookeeper and Kafka dashboards; custom Docker dashboards for 500+
• Docker containers in Red Hat Linux 7.3 kernel; integrated Kubernetes as orchestration engine for Docker containers
• Top analysis and deisgn of the Kafka Broker, Kafka Producer, Kafka Consumer, Schema Registry(100

Sr. Real-time Architect

Confidential, Basking Ridge, New Jersey

Responsibilities:

• Conceived and designed custom POCs using Kafka 0.10 and the Twitter Stream in standalone mode; architected
• the front-end near real-time data pub/sub non-blocking messaging system using the Kafka/Confluent.io Enterprise
• Platform; configured the 10 nodes - 3 Web servers, 4 Kafka brokers and 3 Kafka consumers(Spark Strreaming(DataFrames) with 3 Zookeeper nodes; Kafka brokers able to sustain 1 million wirtes per second peak period for proprietary IoT device analytics plafotom for 4G LTE KI indicator(over 200); researched and codified the Kafka Consumer using KafkaConsumer API 0.10 and KafkaProducer API 0.10(Java); designed the Spark Streaming and KafkaProducer interfaces - for multithreaded partitions and multiple topics by smartphone manufacturer device type; competitive analysis of Storm, Spark, Flink, Samza for processing messages(once only), replay and lost message management, horizontal scalability, security, message sequencing; coordinated Kafka operationa and monitoring(via JMX) with dev ops personnel; formulated balancing leadership strategies and impact of producer and consumer message(topic) consumption to prevent overruns; aggressive monitoring of partitioning versus topic production via JMX interface(s); developed Kafka standalone
• POC’s with the Confluent Schema Registry, Rest Proxy, Kafka Connectors for Cassandra and HDFS(Hadoop 2.0);
• Custom Kafka broker design to reduce message retention from default 7 day retention to 30 minute retention - architected a light weight Kafka broker; incorporated the KStreams API library to develop, code, test, deploy the
• Moving averages of various auto metrics across the IoT application; adjusted window sizes and used various filters to derive the coveted moving average metrics, variance and standard deviation using sliding windowa and variable frames across one second,5 second, one minute, hour using in-memory Java data structures; technical due diligence and research for alternative technologies - Spark Streaming and Cassandra were scrutinized as well
• Created custom test, design and production Spark clusters for the VERUCA - Verizon Universal Communications
• Architecture - Spark clusters exclusively from the AWS Management Console - configuration details, network configuration and security details; architected the S3/EMRFS file systems(11 9's) for the proprietary
• Datasets for 4G LTE analytics for radio signal loss, cellular tower placement, latitude and logitude, 100+ device modem metrics from 30 million devices and 1000 central switches; Spark clusters 1.6.1 in the various environment: s; wrote custom and custom DStreams in Scala for in-flight versus at-rest data for lambda architecture; set up YARN with dynamic allocation for horizontal scaling; calculated different pricing models for reserved, hourly versus spot-pricing; configured EMR for M3/M4 AMI machines for smaller test/ development Spark cluster(8 nodes); separation of computer versus storage AWS frameworks; designed a persistent versus transient architecture - raw Linux server with Spark ML algorithm jobs, test Spark jobs via Zeppelin notebooks; mentored and guided offshore team in troubleshooting and fine tuning Spar
• SQL applications with Ganglia - server load distribution, Spark UI - cached partitions, CloudWatch console metrics, heuristic search through log files of the Spark executors on each Spark worker node and Spark driver; performance tuning of the number of cores, number executors, amount of memory and network bandwidth; code reviews for the optimal Spark application programming; analysis of DAG diagrams for Spark internal execution -
• "lazy" transformations versus actions; examination of Spark UI for job completions, job task completions, cached versus persisted DataFrames; assisted/advised the resident data scientists to configure and codify ML sets and modeling; caching strategies for multi-pass algorithms for better throughput and performance - forest clustering, finite difference calculations, normal distributions, Bayesian statistical modeling; utilized splitable compression to increase throughput from S3 to EC2
• Assisted client in technical interviews of over 20+ potential Big Data architects, technical background checks and review of CVs and resumes; set up Spark coding tests in Scala and Java; installed a Scala IDE test environment to evaluate functional coding precepts; assisted client to interview 10+ dev ops engineers and several Scala developers in knowledge of Linux operations, bash coding skills, troubleshooting techniques and scenario diagnostics
• Collaborated and advised the resident data scientists to extrapolate use cases for machine learning; designed POC
• For Spark applications written in Scala utilize the MLlib - regression, experiments in recommendation engines based on 4G KPI indicators; established performance sandboxes on signal propagation, theoretical versus actual;
• Spark applications examined hundreds of HDFS 5 Mb files across 5 million device sample; derived starndard versus normal versus Poisson distribution models cross-correlated with the cell phone tower lat/long positions across the domestic US;
• Architected full life cycle the Veruca Cassandra ring - developed and designed the entire technology stack,
• versaw and reviewed the APO for .75 mil for the 30 node ring for prod/qa, 15 node ring for dev; orchestrated
• the hardware procurement of the 1 Pb analytics data store ingested through the Kafka pub/sub cluster consisting of
• 10 nodes, peak volume of JSON data coming from 30 million smart phones(android), KPI payload of 3000 bytes/minute; replication factor (X 3); set the DataStax OpCenter for Cassandrta node analysis/troubleshooting;
• Each node consisting of 256 GB RAM, 2 Intel chips each with 8 cores, 6 spindles of 6 Tb/7200 rpm SATA drives
• (JBOD, non-RAID), 2 GB of solid state memory for RHEL version7.2, JRE/JVM rel 1.8.0 92, 10 GigE network with 42U racks, Liebert 440 UPS electrical subsystems, calculated the BTU and heat dissipation and cooling requirements with building HVAC engineers; worked with the radio/telephony 4G engineers to determine
• ptimum query patterns for time-series analysis for 1, 15, 30, 60 minute intervals; query patterns involved
• denormalization of LTE signal tables and device KPIs consisting of 200+ KPI indicators(LAT/LONG coordinates); established and formulated best practices on Cassandra design patterns - atomic distributed counter service, needle in the haystack, anti-patterns; conducted due diligence on performance tuning, read/write consistency of one/QUORUM, schema design; set up bash scripts to centralize Log4J logs by data node; vnode key distribution; synchronized all cassandra.yaml configuration files; oversaw partitioning, secondary indexes, CQL types, use of supercolumns
• Designed and architected the HA solution for Cassandra rings between Basking Ridge, NJ and Dallas, Texas
• for the 30 node ring at each location(T1 dual channel multiplexer link with backup); calculated the optimal snitch based on rack awareness and the NetworkTopologyStrategy, monitored performance in the secondary data center; compaction strategy for SSTables/memtables; designed logging/monitoring systems for the backup data center;
• Spearheaded the POCs for the AWS ecosystem via the AWS Management console, S3 buckets, security - multi-factor authentication, access keys, X.509 s, Eclipse ID plug-in. emphemeral/persistent storage options - Linux and Windows AMI instances, private subnets, designed and deployed Amazon CloudWatch, IAM, Elastic BeanStalk, AWS Simple notification; architected various cloud computing and service design patterns - snapshot, Vagrant, high availab ility - multi-server- floating IP; processing static data - private data delivery, direct storage hosting; patterns for uploading data - write proxy pattern, state ssharing, cache proxy pattern; cloud patterns for operation and maintenance - bootstrap, cloud dependency, stack deployment, weighted transition, hybrid pattern; analyzed t radeoffs for high availability of zones for fault-tolerance versus high availability; set up alarms for CloudWatch for recovery of a failed Linux server, and auto-scaling for guaranteed SLA’a for Linux servers for real-time streaming analytics via Kinesis; analyzed RTO/RDO availabilities for virtual servers for time-lapse of recovery scenarios; established a common network host naming convention with Route 53 with Class C address/VPC subnets; accessed from GitHub Chaos Monkey(Netflix) for arbitrary host/network high latencyperformance problem injections into a custom Dev Hadoop/NDFS cluster(10 nodes) with subsequent post enterprise engineering efforts to monitoring HA via Ganglia; collaborated with sr Web developers for custom
• Web applications - AWS Elastic Beanstalk with multi-container Docker financial applications
• Downloaded, configured Apache Zeppelin binaries/conf for Spark Web clients; integrated Zeppelin daemon with Spark master node, tested and configured Web server with Spark cluster; tested Zeppelin with SparkSQL and Python clients(pluggable interpreters); tested screen sharing functionalities WebSockets, Zeppelin views from

Sr. Cassandra Architect

Confidential, New York City, New York

Responsibilities:

• Created variation of the lambda architecture consisting of near real-time using Spark SQL; Spark cluster 1.4 consisting of 25 nodes running with 200Gb ram/24 Tb, about 1Pb of market data spanning 2000+ stocks with market ticks, number of shares traded, stock price, market ticks over 10 year period; Apache Open source version with Mesos job scheduler; developed, designed tested Spark SQL clients with Scala, PySpark and Java clients; selected best of breed in terms of time-to-deliver; created |Spark Contextx, DataFrames for Cassandra backend and
• HDFS clusters; designed multi-cluster JVM tuning techniques with Jprobe, Nagios/Ganglia for node and cluster tuning; tested Azul Technology Zing versus nation JVM concurrent mark and sweep algorithms; collaborated and advised data scientists for optimum in-memory algorithms using Spark MLlib cluster/interval analysis, pattern recognition, normal versus binomial distribution analysis; probability density and confidence experiments of
• DJ 30 versus SP 500; custom experiments with SP 500 indices with short term SP 500 futures; custom Spark applications designed with accumulators and broadcast variable to gain 4-5% in lowering network “chatter”;Spark cluster in dev environment benchmarked with the Google page-rank algorithm; set up benchmark based on the Daytona sort as reported by the University Of California Berkeley using 1 and 5 Tb; algorithmic comparisons
• f GraphX versus Neo4J of company ownership of Fortune 500 board of directors - business relationshipconnectivity analysis; tested Zeppelin(Spark UI) and Tachyon 0.8.0(off JVM memory management) options ofSpark; configured master/standby servers; configured Tachyon in local machine, standalone, EC2 mode with AWS
• Vagrant plug-in, leveraged Tachyon I/O options for memory life cycle;utilized Spark Scala/Java API/Github; custom design and verification of Spark machine learning algorithms - feature extraction, pipelining, regression analysis, dimensionality reduction (PCA and SVD), k-means clustering
• Comprehensive design, discovery, analysis of the SP Capital IQ software, infrastructure, analytics, hardware in conjunction with the internal architecture review board - concerns of duplicate service calls, improvement and enhancement of existing SLAs to determine, document inaccurate stock quotes and improvements in real-time calculations from the legacy Soalris 9 Unix servers(200+); established comprehensive migration plan to a
• Red Hat Linux(100+) server infrastructure, incorporating complete software stack redesign; collaborated with the
• EA review board for establishing a IQSF(Intelligence Quotient Service Framework) to cover all mutual fund bondequity instruments for corporate, munis, government fixed income instruments via a SOA REST API;
• Weekly meetings with the SOA governance board utilizing the Websphere 7.0 SOA repository(WSRR); detailed service call documentation for input/output message passing, sample service call usage, error code dictionary(systemic, application based, 4000 different financial quotes services with integrated algorithmic dictionary) based upon landmark treatise4 volume set Encyclopedia of Quantitative Finance; established with collaboration of the EAB a comprehensive data dictionary of financial calculation artifacts based upon puts, callsspreads, European, Asian, American style options, cross correlated with the type of risk algorithm used, vinomialBlack-Scholes; applied enterprise architecture “best of breed” methodologies of discrete modularizationseparation of business versus system logic, detailed verification and documentation of existing 800 different application modules by operating system, programming language, frequency of operations runs, relational databases, feeds into data warehouses; established near-term milestones and accountability matrix of market datacollaborated with security architect with state-of-art development of a custom AES 256 cipher key for corporate wide standard of securing customer services for market quotes; comprehensive review, modification a and enhancement of over 500 SOAP service calls to REST API service calls; established and created SOA service call directory(on-line) for bid/ask/rate spreads for commodities - gold, silver, platinum, palladium futures, Forex30/60/90/120/360 for over 100 currencies; assisted peer architect for identifying use cases for Riak and MongoDB annual reports, filings 10K with SEC for the NASDAQ and NYSE for 5 year span, 1.2 M pages in Adobe text, searchable by financial keyword - asset, liability, receivable, payable, shares of stock
• Customized Map-Reduce jobs consisting of multiple HBASE tables using InputFormat Java classes,ptimized M-R jobs by using partitioners for 1 - to-many joins, saving execution time; designed and tested reliability of M-R jobs using unit testing in the HBASE/HDFS dev/qa platforms, unit testing on Mappers, Reducers and integration testing of Mappers, Partitioners and Reducers, designed reporting metrics with counters across the distributed HDFS logs, instituted best practices for defensive programming;
• Set up OOZIE automated job tasks/streams for ETL imports from Oracle batch files into the HDFS data artifacts for market data(bonds, equities) 20 Gb nightly OOZIE job, followed with M-R jobs using the
• OOZIE coordinator, bundle and EL(expression language) for parameters - stock symbols, SP 500; designed custom OOZIE job control options with the OOZIE Java API
• Managed, configured, tuned and continuous deployment of 80 Hadoop nodes in a Red Hat Enterprise edition 5; configured via the AWS console for 2 medium scale AMI instances for the Name Nodess, 78 large scale Data Nodes with 8 Intel i5 cores,3.5 Tb of disk and 350 Mb for JVM per Data Node; automated deployment and Linux system configuration via Chef; utilized 25 different dev op tools to log, debug, discern diagnose performance problems at the database level, Linux daemon level, networking level; set up real-time alerts with custom scripting via awk/fgrep/grep for kernel thread utilization; JVM tuning and garbage collection of short versus long lived Java objects on different generation heap spaces with due diligence on “stop the world gc() algorithms, “mark and sweep”; Chef automated deployment on qa Hadoop cluster of 80 nodes (mirror of prod Hadoop cluster); deployment and configuration of 20 Hadoop nodes on AWS AMI Linux instances

Corporate Security Engineer

Confidential, Harrisonburg, Virginia

Responsibilities:

• Comprehensive review and analysis with a complete top down assessment of corporate records retention, storage,
• destruction policies; complete review of all infrastructure artifacts - databases, middleware, firewalls, DMZ,\ network routers, subnets, honeypots, SSO/LDAP configurations, hardening and rotation policies of corporate and external users of rosettastone.com, Web/Apache server/Ubuntu 11/12 kernel hardening/patch reinforcements; top down review, design and rollout of 3 million customer Confidential and Mastercard numbers state-of-art encryption strategies - two keyTriple DES, Skipjack, NIST/NSA advanced encryption standards and recommendations; review of all corporate email systems for virus and SPAM control, revised strategies and techniques for external countries for currency exchange, foreign payments and auditing, field activity reporting; instituted quarterly ethical hacking procedures, reporting, analysis and follow up IT engineering endeavors; including establishing a corporate security lab to test the latest in pen tests for Windows
• Linux, MacOS and Android/Apple smartphones and tablets; instituted a corporate wide systems responsibility and charter for hardening 4000 company laptops for common encryption/decryption procedures to prevent internal software program theft; instituted and rollout of Kerberos 5 for internal security/ticketing for all J2ee applications running JBOSS 6/6/1 cluster servers for QA and production environments

Hadoop Architect

Confidential, Minneapolis, MN

Responsibilities:

• Launched and promulgated custom business rules engine framework, consolidated and interviewed key
• Comprehensive review of all retail insurance process artifacts, rules engines, message buses, business transformation models, security enforcement of HIPPA /HL7 relating to scrubbing Confidential t data, review of over 5000 + insurance policy due diligence of health and sickness criteria; developed the Aetna Comprehensive Insurance Screening Framework(ACMSF)based upon the precursor of the Affordable Health Care Act; integration of the Kerberos 5 authentication and adjudication policy audit server tracking 3 mil+ inquiries into PPO/HMO/Medicare customers; ACISF built according to the TOGAF 9 methodology; bi-weekly meetings with key executives and stakeholders from the Aetna Enterprise Architecture Review Board for reporting and software and infrastructure component resilience, security, fault-tolerance, performance metrics and SLA’s (4 month effort with business constituencies) resulting in 250 pages of schematics with a 10 EAF steering committee; successful integration into Tibco and Websphere SOA Orchestration server; extensive utilization of best practices of various enterprise integration design patterns for message proxy, modified “spoke and wheel” topology for QA and production messaging frameworks across corporate messaging bus; integrated REST service APIs(over 400+) service calls for insurance policy look ups, claim processing, special APIs created for high speed lookups for insurance actuary tables(Gigaspaces XA) in-memory cache

Trading Architect

Confidential, Pennington, NJ

• Treasury bond portfolio analytics, high performance database accelerators, custom API design for internal staff of 1000+ on-shore/offshore .Net/Java programmers; high performance messaging backbone( messages per second); insurance claim/adjustment SDLC; DR; F5 load balancers

Websphere Solutions Architect

Confidential, NYC, NY

Responsibilities:

• Bank Secrecy Act; Treasury Department cash flow reporting; forex trading(yen dollar currency swaps/hedging options/forward contracts; intelligent lexical parsing of bank wire instructions

SOA Architect

Confidential, Schaumburg, Ill

Responsibilities:

• Sarbanes-Oxley compliance and reporting; ESB/SOA enterprise use case design and compliance; POC/RFP competitive analysis of Tibco/Oracle/IBM SOA stack and ESB selection process; business functionality mappings with Zachmann framework/TOGAF 9 framework; establishment of SOA metrics; SOA dashboard UI design/integration; center of excellence(ESB) – ServiceMix, Apache Websphere ESB, Tibco Active Matrix

Java Security Engineer

Confidential, Washington DC

Responsibilities:

• Confidential PCI compliance; enterprise security of over 120 million “live” credit card numbers; CSO/CIO security directives; penalty for not meeting Confidential PCI compliance malfeasance penalty: 1.5 million per day

Java Architect

Confidential, Greenville, S.C

Responsibilities:

• Sarbanes-Oxley compliance, conversion from Microsoft Windows Active X 120,000 lines of Visual Basic code converted to Websphere 5 J2ee MVC stack in 5 months into production

Systems Architect

Confidential, Detroit/San Francisco

Responsibilities:

• REIT equity fund trading and T-bill buy/sell side trading platform, creation of the the National Economic Data Warehouse application via check imaging; Confidential /Mastercard mag card programming; AI/pattern recognition custom algorithm design

Independent consultant

Confidential, New York

Responsibilities:

• Custom video stream server client development, design and coding

Software Engineer Positions

Confidential

Responsibilities:

• Various systems development and design positions with Fortune 500corporations including Confidential, ATT, General Motors, PrudentialBache, Chrysler, NCR Corporation, Confidential /Banc One, Solomon Brothers, L.F. Rothschild, Allied Signal/Bendix, Itochu Corporation (NKK)