Engineering Manager - Data

Responsibilities:

• Lead, mentor, and grow a team of 8-15 data engineers conducting 1:1s, performance reviews, career planning, and hiring to build a high-velocity, high-quality engineering culture.
• Own the technical roadmap for DTDL's GCP data platform, prioritizing initiatives across data ingestion, transformation, storage, governance, and serving layers.
• Architect and oversee scalable data pipelines using GCP-native services BigQuery, Dataflow, Pub/Sub, Cloud Composer (Airflow), Dataproc, and GCS.
• Drive adoption of data mesh principles, enabling domain teams to own and publish high-quality data products with clear SLAs and discoverability.
• Establish and enforce data quality, observability, and governance standards for data cataloging, lineage tracking, schema management, and SLA monitoring.
• Partner with AI and ML teams to design AI-ready data infrastructure feature stores, vector data pipelines, and low-latency serving layers for agentic systems.
• Drive engineering excellence in code reviews, incident management, on-call rotations, cost optimization, and continuous improvement of platform reliability and developer experience.
• Collaborate closely with product, analytics, and business stakeholders, translating data needs into actionable engineering plans with clear timelines and trade-offs.
• Manage GCP infrastructure costs, monitor spend, and implement optimization strategies across BigQuery slots, Dataflow jobs, and storage tiers.

Requirements:

• 10-14 years of overall experience, with at least 3 years in an engineering management or tech lead role overseeing data engineering teams.
• Deep hands-on expertise with GCP data services BigQuery, Dataflow (Apache Beam), Cloud Composer, Pub/Sub, Dataproc, GCS, and Vertex AI data pipelines.
• Strong background in building large-scale batch and streaming data pipelines with Kafka, Spark, dbt, Airflow, and modern ELT/ETL patterns.
• Solid understanding of data warehouse design BigQuery optimization, partitioning, clustering, materialized views, and cost management.
• Experience with data governance frameworks, data cataloging (Dataplex, Collibra, or similar), data lineage, schema registry, and data quality tooling.
• Proven track record of hiring, developing, and retaining data engineering talent in a fast-paced environment.
• Strong communication skills, able to present technical trade-offs clearly to non-engineering stakeholders and influence cross-functional decisions.