Senior Data Engineer

Designed, built, and maintained scalable batch and real-time data pipelines supporting analytics and machine learning initiatives using Apache Airflow, Apache Beam, Kafka, Snowflake, and Databricks. Architected a modern, performant, cost-efficient data platform and delivered curated datasets that served as trusted organizational sources of truth.

Improved data quality, reliability, observability, monitoring, testing, incident response, governance, and lineage tracking. Advanced CI/CD and infrastructure practices with Terraform and Kubernetes, optimized storage and processing costs, collaborated with product and data science teams, and led workshops on data best practices and tooling.

Engineered data ingestion pipelines and backend data processes using Python and SQL, powering analytics applications across the organization. Designed and deployed Apache Airflow batch-processing components and Kubernetes-based infrastructure for scalable data processing applications.

Used Terraform to streamline environment provisioning and configuration management, while monitoring pipeline reliability and improving performance. Contributed to internal tooling, data documentation, governance, data cataloging, visualization capabilities, architecture reviews, and team training on data engineering practices.

Built and maintained high-volume data pipelines and ETL processes using Python and SQL, enabling efficient delivery of data to analytical tools and dashboards. Designed databases and optimized data storage and retrieval practices to improve performance and team productivity.

Collaborated with cross-functional and analytics teams to create reliable data products, align data definitions and metrics, and provide stakeholders with actionable information. Supported data quality framework development, exploratory data analysis, and third-party data-source integrations.