adsquare GmbH
Staff Data Platform Engineer (m/f/d) at adsquare GmbH in Berlin
Job Description
Intro
At Adsquare, our mission is driven by our core focus:
Passion – Solving complex challenges with great people, tech, and data.
Niche – Location Intelligence for Programmatic Advertisers.
Our core values are integral to everything we do:
Drive: We turn ambition into action.
Resilience: We adapt, persevere, and grow stronger.
No BS: We value honesty, transparency, and clear communication.
Humble: We choose modesty over vanity and let results speak for themselves.
Moral Compass: We do the right thing with fairness, integrity, and respect.
We seek candidates who not only bring top-tier technical expertise but also embody these values in every aspect of their work.
Your Mission
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, run 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 the key architectural calls and setting engineering standards – and you will build the pipelines, design and ship the services. There are no people-management responsibilities.
Our platform processes TB-scale geo-spatial 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.
Your Profile
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.
Must-Have Skills:
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, plus relational databases. You understand partitioning, compaction, schema evolution and query performance.
Pipelines at scale: You have built and run 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.
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 backed by adjacent experience.
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.
What you will do
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 real: Turn the platform into a self-serve foundation that lets squads own, publish and consume data products. Define the standards for data contracts, schema evolution, ownership and production readiness.
Own 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.
Nice to Have
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 serve self-service analytics and AI-driven data access.
Our Software Stack
Python, SQL, Spark, AWS (S3, Glue, Athena, Lambda, Step Functions), Apache Iceberg, Redshift, dbt, Terraform, Kubernetes, Datadog, GitLab CI and AI coding agents. You will help decide what comes next.Why us?
On top of a competitive package:
Hybrid work, and up to 3 months a year working remotely from anywhere
A personal learning budget of 1,200 EUR a 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 tools you need to thrive
Salary Range (Annual On-Target Earnings)
90,000 - 105,000 euros (OTE)Recruiting Process
Take at home quizzes
Introductory call and Values-based interview (30 min)
Tech-Screening Call (30 min)
Practical take-home challenge: a data science problem on realistic data, solved with your usual tools, coding agents included (48h take at home)
Deep-dive technical interview with the Data team: we discuss your take-home challenge solution, further questions about statistics, ML fundamentals, AI, coding (90 min)
Meet the team (30 min)