Job
In this role, you will build a comprehensive data platform, develop pipelines, set standards for data trustworthiness, while making architectural decisions and leading teams.
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Your role in the team
- We are looking for a Staff Data Platform Engineer to build and own the shared data platform that the Data team and the rest of Adsquare build on: ingestion, lakehouse storage, databases, the data catalog, and the agentic tooling that sits on top of it all.
- This is a highly technical, hands-on Individual Contributor (IC) role at the intersection of DevOps and data engineering.
- You will design data systems and also own how they are deployed, operated, and kept healthy in production—infrastructure as code, CI/CD, containers, observability, and cost.
- You will be the technical lead for our data platform—making key architectural decisions and setting engineering standards—and you will build the pipelines, design and ship the services.
- Our platform processes TB-scale geospatial data, audience attributes, and location time series.
- We are moving towards a data mesh, where domain squads own and publish their data as products, and towards a way of working where much of our engineering is done with and by AI agents.
- You will shape both.
- Build the platform, hands-on: Design, build and operate our ingestion and processing pipelines, lakehouse, query layer and databases.
- Make clear trade-offs between latency, correctness, cost, and long-term maintainability.
- Make data mesh a reality: Turn the platform into a self-service foundation that enables squads to own, publish, and consume data products.
- Define the standards for data contracts, schema evolution, ownership, and production readiness.
- Own the catalog, metadata, and lineage: Make our data discoverable, documented, and trusted—with lineage, quality signals, and clear ownership—so that both people and AI agents can find and use the right data.
- Build agents for developers: Design and ship agents, MCP servers, and tools that let engineers and agents explore data, validate pipelines, debug incidents, and automate routine operations.
- Set the team's standards for agentic coding: how we use coding agents, share skills, review agent-written code and keep guardrails in place.
- Reliability, observability and cost: Build monitoring, alerting and data quality checks that catch problems before they reach downstream products, and keep cloud costs visible and under control.
- Lead and multiply: Drive architecture decisions across squads, raise the bar through design and code reviews, and mentor Senior, Mid and Junior engineers.
Our expectations of you
Qualifications
- We are looking for a builder: an engineer with deep platform knowledge who still loves shipping code, and who is excited about how AI agents are changing data engineering.
- You understand partitioning, compaction, schema evolution, and query performance.
- Pipelines at scale: You have built and operated batch and event-driven pipelines at TB scale (e.g., Spark, Glue, Step Functions, Airflow, or Dagster), including backfills, failure recovery, and data quality checks.
- Agentic engineering: You work with AI coding agents every day and get real leverage from them, and you have built (or are eager to build) LLM agents, MCP servers, or tools on top of data systems.
- Software engineering & DevOps: Strong production Python (tested, typed, maintainable) and expert SQL.
- A DevOps mindset: you treat infrastructure as code and are hands-on with AWS, Terraform, containers/Kubernetes, CI/CD, and production monitoring.
- Technical leadership: You drive consensus across teams, explain architectural trade-offs clearly to engineers and non-technical stakeholders, and make the people around you better.
Experience
- Experience: 7+ years in Data Engineering, Data Platform or Backend Engineering, including significant time at senior or staff level with platform-wide scope—and still hands-on.
- Lakehouse & database design: Deep experience designing and operating lakehouse and database architectures — open table formats (e.g., Iceberg), Parquet, object storage, query engines such as Athena/Trino or Redshift, as well as relational databases.
- Data mesh & data catalog: Hands-on experience with data catalogs and metadata (e.g., DataHub, OpenMetadata, AWS SageMaker Catalog, Glue Data Catalog, Unity Catalog), lineage, data contracts, or data products—or a strong, demonstrable interest in data mesh supported by relevant adjacent experience.
- Geo-spatial data: Experience with spatial indexes and tools (e.g. H3, S2, PostGIS) and location time-series data.
- Ad tech: Experience in programmatic advertising, audience/segment data, or privacy and consent requirements.
- Semantic layer: Experience building metrics or semantic layers that support self-service analytics and AI-driven data access.
What we offer
- Hybrid work, and up to 3 months a year working remotely from anywhere.
- A personal learning budget of EUR 1,200 per year.
- 30 vacation days a year.
- Urban Sports Club membership, company pension scheme and relocation / home office budget.
- Regular team and company events organised by our People team.
- Latest hardware and all the tools you need to thrive.
Benefits
Work-Life-Integration
Health, fitness & fun
Topics you will work on
Job locations
About your employer
adsquare GmbH, based in Berlin, is a globally operating company specializing in audience and location intelligence. It assists brands, advertisers, and media agencies in planning, activating, and optimizing their campaigns based on data. The company offers solutions that prioritize privacy in marketing and advertising.
- Established Company
- Hybrid, Onsite
- Internet, IT, Telecommunication
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