Lead, mentor, and scale a team of engineers focused on high-throughput data ingestion, processing, and storage solutions.
Own the data roadmap by defining technical strategy, setting priorities for data architecture, and aligning with product and business stakeholders.
Drive data reliability and observability strategy to ensure high data quality, lineage tracking, and uptime for mission-critical pipelines.
Champion best practices in data ops, including version-controlled schemas (dbt), CI/CD for data pipelines, and automated testing.
Drive innovation in ML-readiness by partnering with the ML Platform team to provide clean, feature-rich datasets for model training and inference.
Foster a culture of data stewardship, ensuring data is discoverable, well-documented, and accessible across the organization.
Partner with Security, Compliance, and Legal teams to implement robust data privacy and SOX-compliant access controls.

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.
7+ years of experience in data engineering and distributed systems, with at least 2+ years in a technical leadership or management role.
Proven success building and scaling modern data stacks on cloud providers (AWS preferred), including experience with Snowflake.
Expertise in distributed processing (Spark, Flink, or Kafka) and workflow orchestration tools such as Airflow.
Hands-on experience developing and debugging complex data transformations using Python and high-performance SQL.
Strong understanding of data modeling and experience managing "Data as a Product" for internal consumers.
Familiarity with data governance tools and practices (cataloging, lineage, and PII masking).
Proven track record managing teams that deliver scalable, resilient data architectures in fast-growing environments.
Excellent communication skills with the ability to translate complex data challenges into business impact for non-technical stakeholders.

Data Engineering Manager

Key skills