StarFit.AIFor Data Leaders
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Data Engineer / Big DataDeliver Results (Amazon)Extreme Scalability & Performance Optimization
Behavioral Interview Question

"Tell me about a complex data migration where you faced unexpected performance bottlenecks and tight deadlines."

Spearheaded the zero-downtime migration of 45TB of legacy Teradata data into AWS Redshift, refactoring 120+ BTEQ scripts and reducing batch window time by 62%.

S

Situation (Scale & Risk)

Our enterprise global supply chain analytics platform relied on an on-premise Teradata cluster running at 94% storage capacity with annual licensing costs exceeding $1.4M. Executive leadership mandated a complete cloud migration to AWS Redshift within 4 months.

Timeline & Scale: 4-month hard deadline prior to enterprise licensing renewal; 45TB historical data, 120+ daily production ETL workflows, and 450 downstream business stakeholders.
Risks & Stakes: Missing the deadline would force a non-negotiable $650K license renewal penalty, and any data inconsistency would compromise global supply-chain procurement reporting.
T

Task (Ownership & Scope)

I was the Lead Data Engineer responsible for designing the migration blueprint, refactoring legacy BTEQ scripts, establishing continuous data reconciliation, and executing a zero-downtime cutover.

Accountability: Personally accountable for pipeline refactoring, Redshift table partitioning/distribution design, and end-to-end data parity validation.
Success Metric: 100% data fidelity, zero disruption to daily morning dashboards (SLA 06:00 AM EST), and reducing overall batch execution time by at least 40%.
A

Action (Technical Execution)

  • Conducted automated AST parsing of 120+ Teradata BTEQ scripts to extract business logic, translating proprietary Teradata functions (e.g., QUALIFY, TD_MONTH_BEGIN) into Redshift SQL equivalents.
  • Redesigned Redshift distribution and sort keys: applied COMPOUND sort keys on transaction timestamps and DISTSTYLE KEY on customer_id, eliminating costly cross-node data broadcasting.
  • Engineered an incremental dual-write ingestion pipeline using AWS DMS (Database Migration Service) and S3 staging buckets with parallel COPY commands and GZIP compression.
  • Implemented an automated checksum & row-count reconciliation script in Python (Pandas/Great Expectations) that flagged discrepancies across 1.2 billion rows prior to final cutover.
Key Architectural Decision: Chose AWS Redshift RA3.4xlarge instances to decouple compute from storage, allowing dynamic cluster scaling during peak end-of-month financial reconciliation.
R

Result (Hard FinOps & ROI)

  • ⚡Delivered the cutover 2 weeks ahead of schedule, saving the organization $650,000 in legacy renewal fees and cutting annual infrastructure cost by 54% ($760K/yr).
  • ⚡Reduced the daily batch ETL execution window from 7.5 hours down to 2.8 hours (62% performance improvement).
  • ⚡Achieved 99.99% data reconciliation parity across all 45TB without a single production data loss incident.
Long-Term Standard: Instituted automated CI/CD schema migration tests with Terraform and Jenkins, which became the global engineering standard across all data engineering teams.
#AWS Redshift#Teradata#Data Migration#ETL Optimization#Python#SQL

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