Senior Data Engineer

Responsibilities

• Design, build, test, and deploy scalable and reliable data pipelines that power analytics and product decision-making.
• Own medium-sized data platform initiatives end-to-end, from initial design through production deployment and operational support.
• Design, migrate, and maintain data workflows in Apache Airflow, including supporting migration of legacy ETL systems to modern orchestration patterns.
• Ensure pipeline reliability by proactively monitoring workflow SLAs for freshness, quality, and performance, and resolving failures efficiently.
• Implement and utilize monitoring systems to detect pipeline failures, schema drift, and data quality anomalies.
• Participate in on-call rotations and contribute to incident response and root cause analysis for data incidents.
• Apply best practices in warehouse performance and cost optimization, including partitioning, indexing, and efficient data modeling to control BigQuery spend.
• Build maintainable, modular data models and pipelines using reusable patterns and shared components across the team.
• Partner closely with Analytics Engineering, Data Science, and business stakeholders to deliver durable improvements across the ingestion, transformation, modeling, and serving layers of the data platform.
• Contribute to operational excellence through documentation, monitoring improvements, and participation in postmortems and reliability initiatives.

Requirements

• 5+ years of experience in data engineering, with a track record of delivering reliable production data pipelines and systems.
• Strong experience designing and maintaining orchestration workflows using Apache Airflow.
• Experience building scalable data pipelines using modern cloud data platforms such as BigQuery and tools such as DBT.
• Strong understanding of data modeling, schema design, and building maintainable, modular data systems.
• Experience implementing CI/CD best practices for data pipelines, including automated testing, validation, and deployment workflows to ensure reliable and repeatable production releases.
• Experience monitoring and operating production data systems, including pipeline observability, data quality checks, and incident response.
• Ability to identify performance and cost risks in large-scale data systems and implement optimizations.
• Strong collaboration skills and experience working with cross-functional partners including analytics engineers, data scientists, and product teams.
• Proven ability to independently execute medium-sized projects and deliver reliable, production-grade systems.

Nice-to-Haves

• Familiarity with Kubernetes-based data infrastructure, including deploying and operating containerized data services and workflows in a production environment.
• Experience migrating legacy ETL systems to modern orchestration frameworks.
• Familiarity with observability and monitoring tools for data pipelines (e.g., Datadog or similar).
• Experience operating data systems with strict SLAs for freshness, reliability, and cost efficiency.