data engineeringleadershippythonsqlapache sparkapache flinkapache kafkagoogle cloudbigqueryterraformci/cdargoCDdockerlakehousedeltaicebergadtechprogrammaticstreamingbatch processingdata pipelines
Key details
Salary
Not specified
Employment type
Full-time
Seniority
Senior
Years experience
5-10
Location
Dublin, Ireland
Full job description
Verve is hiring a Data Engineering Manager in Dublin to lead and expand the European engineering hub. The role involves managing the global Mobile SDK chapter with teams in Hamburg, Bangalore, and Dublin. Responsibilities include supervising the local data engineering pod, managing team capacity and hiring, fostering a high-performance culture, planning and executing multi-quarter initiatives such as data lakehouse re-architecture and CI/CD automation, refining engineering processes and standards, collaborating with Product Management and other teams, and maintaining hands-on technical involvement (50-70%). The candidate will also handle production responsibilities including monitoring, on-call support, incident management, and designing scalable data systems for high-throughput streaming and batch pipelines. Required skills include 8+ years in Data Engineering with leadership experience, AdTech industry knowledge (programmatic auctions, RTB data), expert Python and SQL skills, experience with Apache Spark, Flink, Kafka, Google Cloud data stack (BigQuery, GKE, GCS, Dataflow/Vertex AI), Terraform, CI/CD tools like ArgoCD, containerization with Docker, knowledge of lakehouse/open table formats (Iceberg/Delta), and familiarity with analytics/BI tools (Druid, Looker, Rill/Turnilo).
What you'll do
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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
