Data Engineering Manager

What you'll do

• Provide full supervision and support for the local data engineering pod, spanning both data-platform and analytics/reporting work streams
• Manage team capacity, lead hiring to backfill and grow the pod, and define team charters supporting a fast-evolving data platform roadmap
• Foster a high-performance culture across a distributed organization (Bangalore and EU), onboarding recent hires and establishing operational discipline (on-call/IRM, runbooks, incident response)
• Plan and execute multi-quarter initiatives with many interdependencies (e.g., data lakehouse re-architecture using Apache Flink on GKE, dedicated CTV late-impression pipeline, CI/CD automation with ArgoCD) ensuring alignment with architectural standards
• Refine engineering processes, methodologies, and technical standards: code quality, infrastructure-as-code, deployment automation, observability, and cost optimization
• Partner closely with Product Management, Backend Engineering, Data Science, and Analytics to align platform roadmap with business objectives
• Stay active in technical implementation (50-70% hands-on): designing pipelines, delivering new product features, reviewing architecture and code, addressing tech debts, troubleshooting production data issues
• Undertake production responsibilities including setting up monitoring and alerts, on-call support, troubleshooting, incident management and communication, and postmortem
• Independently design maintainable and scalable data systems for complex, high-throughput streaming and batch pipelines while managing infrastructure costs

Requirements

• Minimum of 8 years of experience in Data Engineering, with a focus on leadership in technical settings
• AdTech industry expertise (specifically programmatic auctions and high-volume bid/impression/RTB data) is strongly preferred
• Expert knowledge of Python and SQL
• Deep, hands-on experience with distributed data processing and streaming: Apache Spark / Spark Streaming and Apache Flink
• Strong experience operating high-throughput event streaming and messaging with Apache Kafka
• Proficiency with the Google Cloud data stack (BigQuery, GKE, GCS, Dataflow/Vertex AI) and infrastructure-as-code (Terraform)
• Experience building and operating CI/CD for data pipelines (e.g., ArgoCD, containerized/Docker deployments)
• Working knowledge of lakehouse and open table formats (e.g., Iceberg/Delta) and the trade-offs between real-time and batch architectures (latency, cost, governance)
• Familiarity with analytics/OLAP and BI tooling (e.g., Druid, Looker, Rill/Turnilo) and dimensional data modeling

Tech stack

Benefits