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CV -- Priya Nandakumar

Location: Amsterdam, Netherlands Email: priya@example.com LinkedIn: linkedin.com/in/priyanandakumar Portfolio: priyan.dev GitHub: github.com/priyanandakumar

Professional Summary

Data engineer with 6 years building reliable analytics and ML feature pipelines. Shipped lakehouse-style platforms (Iceberg, dbt, Spark) for a global fintech, cutting nightly batch windows and enabling near-real-time risk dashboards. Strong in SQL, Python, data modeling, and pragmatic data quality.

Work Experience

NorthRiver Payments -- Amsterdam, Netherlands

Senior Data Engineer 2020-2025

  • Owned core ingestion from OLTP to the analytics lake (Airflow, Spark on Kubernetes): CDC streams plus batch backfills with idempotent loads and schema evolution
  • Introduced dbt for the warehouse layer: standardized tests, documentation, and CI for SQL changes; reduced broken dashboards from recurring incidents to rare exceptions
  • Partnered with ML teams on feature stores and offline training datasets: SLAs, partitioning strategy, and PII handling aligned with legal review
  • Led data quality program: Great Expectations checks at ingest, anomaly alerts, and quarterly reviews with domain owners
  • Mentored 3 engineers on SQL performance, incremental models, and incident response for pipeline failures

BrightMetrics BV -- Utrecht, Netherlands

Data Engineer 2017-2020

  • Built ETL from SaaS APIs and files into PostgreSQL and Redshift; migrated critical jobs from cron scripts to Airflow with retries and alerting
  • Designed star-schema marts for finance and product analytics; supported self-serve BI (Looker) adoption
  • Improved warehouse costs by pruning unused tables, compressing wide fact tables, and rightsizing clusters after usage analysis

Projects

  • dq-patterns (Internal playbook) -- Reusable patterns for row-level checks, freshness SLAs, and ownership RACI; presented at internal data guild
  • streamline-blog (Writing) -- Short posts on incremental dbt models and backfill safety for event data

Education

  • MSc Computer Science (Data Systems track), University of Amsterdam (2017)

Skills

  • Pipelines: Airflow, Dagster basics, dbt, Spark, Kafka (consumer patterns)
  • Storage: PostgreSQL, Snowflake, S3, Apache Iceberg
  • Languages: SQL, Python, Bash
  • Ops: Docker, Kubernetes basics, Terraform (modules for data jobs), GitHub Actions
  • Practices: Data modeling (dimensional + wide marts), cost awareness, documentation-as-code