SKILLS
- Python
- PySpark
- Terraform
- MLflow
- Time-Series Data Processing
- MongoDB
- GitHub Actions & CI/CD
- Power BI
- Git
- SQL
- Apache Kafka
- AWS
- Argo
- dbt
- DynamoDB
- Data Quality & Validation
- Tableau
- Cross-Functional Collaboration
- PostgreSQL
- Apache Airflow
- Kubernetes
- Prometheus & Grafana
- Docker
- Data Modelling
- Medallion Architecture
- Amazon QuickSight
- Stakeholder Communication
PROFESSIONAL EXPERIENCE
09/2025 - 03/2026
Role: Business Intelligence Engineer
Customer: Amazon, Germany
Tasks:
- Kept 500GB+ of analytical data accurate and traceable for 200+ users, as measured by adoption of 10+ governed KPIs across 3 business units, by architecting a governed data lake with data validation and quality control frameworks.
- Sustained a 99% change-success rate on production pipeline deployments, as measured by release stability, by taking full ownership of monitoring and stabilising production data workflows.
- Delivered self-service dashboards adopted org-wide, as measured by reduced manual reporting requests, by building governed data models validated against agreed acceptance criteria.
- Cut manual troubleshooting by 10 hours/week, as measured by reduced incident volume, by setting up proactive data quality monitoring and alerting that flagged deviations before they reached production.
- Reduced rework on analytical requirements, as measured by validated acceptance criteria, by partnering with business and engineering stakeholders to translate ambiguous asks into reusable data models.
03/2024 - 09/2024
Role: Data Engineer Intern
Customer: Amazon, Luxembourg
Tasks:
- Cut processing time by 30% for 100+ downstream analysts, as measured by end-to-end pipeline latency across 1TB+ of data, by building near real-time ETL/ELT pipelines integrating 6 enterprise systems into a centralised data lake.
- Eliminated 95% of manual data-processing intervention, as measured by validation coverage, by implementing automated scheduling, validation, and monitoring across production pipelines.
- Reduced query latency by 15% across distributed environments, as measured by system performance under load, by optimising large-scale SQL and PySpark transformations over high-volume, timestamped operational datasets.
- Accelerated onboarding of new data sources, as measured by reusable pipeline components, by documenting system interfaces and data flows for clean handover.
08/2021 - 09/2022
Role: Data Engineer (A.S.E)
Customer: Tata Consultancy Services, India
Tasks:
- Contributed to a 20% reduction in production defects, as measured by defect recurrence rate, by building modular Python data ingestion and SQL transformation pipelines with automated testing and Git version control.
- Saved 8 hours/week in manual data preparation across a team of 12, as measured by pipeline throughput, by maintaining 5+ REST API integrations with structured technical documentation.
- Compressed defect turnaround from 3 days to same-day, as measured by throughput across 50+ Jira-tracked tickets, by investigating and resolving recurring data quality issues.
- Improved stakeholder decision-making, as measured by adoption, by designing 15+ operational dashboards translating complex system data into actionable insights for non-technical teams.