Adam Olkowski
I’m a DataOps and Data Platform Engineer with around 8 years of software engineering experience, including 6+ years building and operating production-grade data pipelines for enterprise clients.
About
I’m a DataOps and Data Platform Engineer with around 8 years of software engineering experience, including 6+ years building and operating production-grade data pipelines for enterprise clients. I design and run orchestrated workflows (notably with Apache Airflow), implement reliable ingestion and transformation layers, and make sure pipelines can be backfilled, recovered, and monitored with clear alerting and failure handling.
I work across the full delivery lifecycle: turning stakeholder requirements into governed data operations, building on GCP and cloud-native tooling (BigQuery, Dataflow), and operating workloads on Kubernetes using infrastructure as code and CI/CD. I also focus heavily on data quality and governance—using validation, reconciliation, lineage, and access controls—so analytics and ML consumers get dependable, traceable data products.
- 8 years' experience
- Joined October 2026
Industry experience
- Financial Services
- Software & Internet
- Professional Services
Work experience
Built governed Lakehouse data platforms and Python pipelines for enterprise clients, owning data quality, lineage, infrastructure as code, CI/CD and production support. Developed PySpark and Python ingestion and transformation pipelines on Databricks with Delta Lake, processing ~50M records/day through bronze/silver/gold layers using explicit schemas, validation, retries, and traceable failure handling. Designed Lakehouse patterns with Delta Lake, Unity Catalog and Databricks Workflows across ~6 data domains and ~120 curated tables, enforcing access controls, lineage and cataloging. Implemented automated data quality/observability using schema checks, null/duplicate rules and pytest suites (200+ assertions) to detect drift before downstream impact. Managed release automation with GitHub Actions and Terraform across dev/test/prod, and supported ML feature pipelines, MLflow tracking/model promotion and production monitoring for multiple models.
Owned Python and PySpark pipelines, Airflow orchestration, shared pipeline frameworks, data quality and infrastructure automation for high-volume regulated banking and payments platforms across AWS, Azure and GCP. Designed Apache Airflow workflows for ~60 DAGs with scheduling, dependencies, backfill, recovery and alerting. Built production ETL/ELT pipelines ingesting ~20M transactions daily with idempotent processing, validation, retries and structured error handling. Implemented validation and reconciliation comparing upstream/downstream datasets across 15+ source feeds to flag missing records, duplicates and inconsistent states. Standardized infrastructure as code and CI/CD using Terraform, Docker, Kubernetes, Helm, Git and GitHub Actions; supported monitoring with Datadog/Prometheus/Grafana/ELK and optimized SQL/lakehouse-style models for correctness and partitioning.
Developed C# and T-SQL backend components, APIs and data validation for payment and e-commerce workflows under PCI-DSS. Implemented dataset validation and reconciliation to detect malformed records, duplicates and inconsistent states in data used by reporting and downstream integrations. Built RESTful APIs to maintain stable data contracts and troubleshoot integration failures across 10+ enterprise boundaries. Optimized SQL and backend processing, reducing average query response time by ~30% through indexing and execution-plan analysis while preserving correctness. Participated in secure coding, automated testing, code reviews, documentation and incident investigation with audit-ready delivery; contributed to modernization planning for a legacy PHP application.
Education
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