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.

Experience
10-14 yrs