Manager Data Engineering Resume Boston, MA - Hire IT People

PROFILE SUMMARY:

My 12+ years of experience in technology, across multiple industry domains, has helped me understand the value of data.
As a Machine Learning architect, I design and integrate machine learning solutions with enterprise data and applications.
I work extensively in designing distributed ML, big data architectures and developing solutions optimized for public cloud.
I've lead data - driven transformations for a variety of organizations - from startups to fortune 500 companies.
I collaborate with business and technology stakeholders across the firm to evaluate solutions and develop consensus on technical direction.

CORE COMPETENCIES:

KEY SKILLS:

Programming Skills: Python, Shell Scripting, SQL, JSON, XML, Java, Cobol

Machine Learning Tools: Tensorflow, Keras, Scikit: learn, Pandas

Big Data Tools & Platforms: Hortonworks (HDP), Apache Beam, Spark, Oozie, Pub/Sub, Hive, Pig

Other Tools: Jenkins, Git, Tableau, Google Data Studio

Database: BigQuery, Hive, Cassandra, MySQL, SQL Server, DB2

Cloud Platforms: Google Cloud Platform, Microsoft Azure Cloud

Domain: Retail, Finance, Insurance, Education, Automotive

PROFESSIONAL EXPERIENCE:

Confidential, Boston, MA

Manager Data Engineering

Responsibilities:

Lead teams of data engineer & scientists for designing & implementing machine learning solutions.
Initiated set-up of applied machine learning engineering practice within the organization.
Identified opportunities & implemented solutions to leverage machine learning & cloud for personalization, scaling operations, and optimizing marketing efforts, for various clients.
Acted as a solution architect for multiple clients, for solving their data & analytics challenges.
Design software architecture to integrate ML models with enterprise data and applications.
Define real-time and batch processing architectures using Big Data tools and Cloud Platforms.
Conduct assessments to determine best cloud platform for implementing a cost effective and scalable data & analytics solution.
Assess current stage of data management, identify gaps, provide recommendations and develop roadmaps for modernizing data architecture
Work with client’s data science and data engineering teams to lay out the vision for current and future solutions.
Assist in sales cycle by working on RFPs, PoCs and creating estimates.
Mentor data engineers and scientists to train them on best practices for automating data & analytics workflows.

Confidential, Boston, MA

Sr. Technical Lead

Responsibilities:

Architect & develop enterprise data lake for a large Insurance client, to bring in data from various business units - leveraged Apache Spark, Hive, HDInsight, and Azure Cloud.
Lead architecture design and implementation of data lake for a major Banking client, using Hortonwork Development Platform.
Design architecture to integrate data engineering and data science workflows.
Architect ‘infrastructure as a code’ to support the deployment of data solutions on cloud platforms.
Translate complex functional requirements into detailed design.
Architect big data solutions to optimize processing of very large datasets.
Train and develop a pool of developers to work on big data projects.
Developed an efficient cross-functional team to keep up with the rapid developments in Big data arena.
Explore emerging tools and techniques in big data analytics space.
Develop pre-sales pitches and subsequent proof-of-concepts (PoCs) for multiple clients.

Confidential, Miami, FL

Technology Analyst

Responsibilities:

Database design, optimizing SQL queries and developing solution architecture.
Design process for automating system monitoring and log analysis using shell scripting.
Build ETL pipeline for processing high volume data,
Migrating legacy batch-heavy operations to real-time data processing and storage.
Legacy modernization and demand management initiatives.
Liaise with the client to understand the business requirements.
Mentoring programmers and offshore team.
Train on emerging technologies, including big data, Hadoop & NoSQL.