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Data Technology Analyst Resume

Coon Rapids, MN


  • Accomplished IT personell with expertise in data analysis, performance tuning, crisis management, and high availability to increase reliability, maximize productivity, and introduce system efficiencies supporting next - generation database solutions in oracle database enterprise.


Operating Systems: UNIX - Solaris, HP UX, AIX, Red Hat 6.5 (and older), Linux 3.17 (and older), and Windows

RDBMS: Oracle 10g 11g Releases 1 and 2, 12c

Tools: SQL*Plus, EM, Grid Control, SQL*Loader, RMAN, Tracing, StatsPack, Explain-Plan, ASM Grid Control, AWR, Scheduler, Data Pump, Shell Scripting, ASH, TOAD, SQL Developer, IBM Clear Case, IBM Clear Quest.


Confidential, Coon Rapids, MN

Data Technology Analyst


  • Started and managed biweekly optimization meetings with enterprise user groups to address and resolve performance-related issues with a high degree of success.
  • Successfully set up a two-node cluster and RAC data warehouse on Linux (Oracle) using ASM.
  • Identified, led, and established automation of repetitive processes to minimize errors.
  • Completely eliminated space errors by implementing proactive space monitoring and effective space allocation.
  • Greatly improved performance in daily transactions by identifying and reorganizing index on major tables.
  • Performed migration from 11gR2 to 12c.
  • Formulated recommendations about optimal storage configurations for senior management.
  • Identified and reduced/eliminated non-idle wait events such as scattered file read, sequential file read wait event, log file completion, in queue, and buffer latch waits.
  • Exported and imported data using export/import utilities.

Confidential, Bowie, MD

Oracle Database Administrator


  • Provide Oracle database administrative support to various user groups (application developers, data managers etc.); create and maintain Oracle database objects (schemas, tables, indexes etc.).
  • Analyze and tune voluminous SQL code.
  • Tuned database objects to run smoothly by analyzing query paths (creating explain path tables) and using efficient indexing.
  • Perform capacity planning in terms of memory and disk usage, establishing benchmarks for business and technical performance.

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