Data Engineering Manager

About the role

Verve has created a more efficient and privacy-focused way to buy and monetize advertising. Verve is an ecosystem of demand and supply technologies fusing data, media, and technology together to deliver results and growth to both advertisers and publishers-no matter the screen or location, no matter who, what, or where a customer is. With 30 offices across the globe and with an eye on servicing forward-thinking advertising customers, Verve's solutions are trusted by more than 90 of the United States' top 100 advertisers, 4,000 publishers globally, and the world's top demand-side platforms. Learn more at verve.com .

Responsibilities

• 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 that support a fast-evolving data platform roadmap.
• Foster a high-performance culture across a distributed organization (Bangalore and EU), onboarding recent hires and establishing strong operational discipline (on-call/IRM, runbooks, incident response).
• Plan and execute multi-quarter initiatives with many interdependencies (e.g., the data lakehouse re-architecture (Apache Flink on GKE), the dedicated CTV late-impression pipeline, and CI/CD automation (ArgoCD)) ensuring alignment with architectural standards.
• Refine the team's 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 the platform roadmap with business objectives.
• Stay active in technical implementation (estimated 50-70% hands-on): designing pipelines, delivering new product features, reviewing architecture and code, addressing tech debts, and troubleshooting production data issues.
• Undertake production responsibilities including setting up monitoring and alerts, oncall support, troubleshooting, incident management and communication, and postmortem.
• Independently design maintainable and scalable data systems for complex, high-throughput streaming and batch pipelines. Be aware and manage the infrastructure cost of data systems.

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.

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