Enterprise Data Architect / Lakehouse ArchitectInvent and Simplify (Amazon)Innovation & Process Simplification
Behavioral Interview Question
"Describe a complex enterprise data architecture you designed from scratch to solve data fragmentation and governance issues."
Designed a unified Medallion Lakehouse architecture on Databricks & Delta Lake for a FinTech platform processing 250M daily transactions, cutting query latency by 74%.
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Situation (Scale & Risk)
A fast-growing FinTech company had fragmented data silos across 8 relational databases, 4 S3 buckets, and legacy Snowflake warehouses, resulting in duplicate data pipelines, stale reports, and compliance vulnerabilities under GDPR/CCPA.
Timeline & Scale: 6-month initiative spanning 5 product engineering units, ingesting 250M daily events (approx. 3.2TB/day) across fraud, payments, and risk management.
Risks & Stakes: Audit findings flagged potential regulatory fines up to €4M due to inability to enforce "Right to be Forgotten" across siloed databases.
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Task (Ownership & Scope)
I served as the Lead Data Architect to design and govern a centralized, compliant Medallion (Bronze/Silver/Gold) Lakehouse on Databricks Delta Lake and AWS S3.
Accountability: Accountable for architecture blueprints, Unity Catalog governance policies, schema evolution strategy, and cost-per-query benchmarks.
Success Metric: Single source of truth for 150+ analysts, GDPR compliance SLA under 24 hours (down from 14 days), and 50%+ reduction in warehouse query latency.
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Action (Technical Execution)
- Designed Bronze (raw ingestion via Autoloader/Kafka), Silver (cleaned, deduplicated 3NF entities), and Gold (Star Schema business aggregates) data layers using Delta Lake ACID transactions.
- Enforced fine-grained column-level and row-level access controls using Databricks Unity Catalog, ensuring PII data was masked dynamically for unauthorized roles.
- Engineered an automated GDPR deletion framework utilizing Delta Lake OPTIMIZE and VACUUM features, executing user-level erasure requests in 12 minutes.
- Established Liquid Clustering on high-cardinality keys (account_id, timestamp) to replace legacy partitioning, dramatically speeding up multi-tenant queries.
Key Architectural Decision: Selected Delta Lake over Apache Iceberg due to tighter integration with native Delta Live Tables (DLT) and Unity Catalog data lineage tracking.
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Result (Hard FinOps & ROI)
- ⚡Reduced median query response time from 18 seconds to 4.6 seconds (74% improvement) across all BI dashboards.
- ⚡Cut monthly cloud data spend by $38,000 (31% cost reduction) by consolidating 4 redundant pipelines and utilizing automated cluster shutdown policies.
- ⚡Achieved 100% compliance on the subsequent independent SOC-2 Type II and GDPR regulatory audit with zero findings.
Long-Term Standard: Created a standardized Data Contract template (YAML-based) enforced at the ingestion boundary, preventing schema breaks from upstream microservices.
#Databricks#Delta Lake#Lakehouse#Unity Catalog#AWS S3#Data Governance