> Markdown version of [/jobs/ext/1344454-lead-data-engineer-data-migration-lakehouse](https://www.wearedevelopers.com/jobs/ext/1344454-lead-data-engineer-data-migration-lakehouse). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Data Engineer - Data Migration & Lakehouse - **Company:** SQUER Solutions GmbH - **Location:** München, Germany - **Experience:** Expert - **Salary:** €81,000.0 - **Contract:** Permanent contract - **Skills:** Unity 3d, Artificial Intelligence, COBOL (Programming Language), Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Migration, Mainframes, Meta-Data Management, SQL Databases, Large Language Models, Apache Spark, Data Lakes, Data Pipelines, Legacy Systems, Databricks - **Published:** July 19, 2026 - **Apply:** https://de.indeed.com/viewjob?jk=a608ebd2c5510e1a ## About the Role * Must-Haves + Migration Track Record: 5+ years in data engineering, with several large-scale legacy-to-cloud/lakehouse migrations led independently. + Databricks Depth: Hands-on mastery of the Databricks ecosystem - Spark, Delta Lake, Unity Catalog, Lakeflow Declarative Pipelines, and Asset Bundles for CI/CD-style deployment. + Data Modeling & Reconciliation: Strong command of data modeling and schema mapping across heterogeneous systems, from mainframes to legacy ERPs. + Discovery Experience: Experience in discovery/assessment phases end-to-end - interviewing SMEs, identifying gaps between legacy systems and target architecture, turning ambiguity into concrete requirements. + Data Quality Rigor: Practical experience with data quality frameworks and automated validation (e.g., Great Expectations, dbt tests, custom reconciliation tooling). + Communication & Risk Ownership: Ability to break down complex topics and surface risks early and clearly to both technical and non-technical stakeholders. + AI-Assisted Workflows: Comfort using AI/LLM-assisted tooling in a data pipeline context., + Experience with legacy system archaeology (undocumented COBOL/mainframe systems, EDI, proprietary ERPs) + Exposure to agentic AI + Metadata management or data catalog tooling beyond Unity Catalog + Experience building or evaluating LLM-based mapping/matching tools (schema matching, entity resolution) ## Description You thrive on turning decades of legacy complexity into clean, modern data architecture. You feel at home navigating undocumented mainframes, tangled schemas, and ambiguous specs - and you know how to translate that chaos into a clear technical path forward. If you love owning complex migrations end-to-end, building trust with stakeholders through clarity and honesty, and shaping how data moves from legacy into the lakehouse era - this is for you., * Migration Ownership: Lead large-scale migration projects from legacy systems (ranging from SQL over ERP to mainframes) into modern cloud/lakehouse targets, taking full technical responsibility. * Databricks Architecture: Design and implement robust pipelines using Spark, Delta Lake, Unity Catalog, Lakeflow Declarative Pipelines, and Databricks Asset Bundles for CI/CD-style deployment. * Data Modeling & Reconciliation: Own data modeling, schema mapping, and reconciliation across heterogeneous systems, resolving structural mismatches independently. * Discovery & Assessment Leadership: Reviewing source documentation, interviewing SMEs, producing gap analyses, and translating ambiguous or outdated specs into concrete technical requirements. * Data Quality & Validation: Establish data quality frameworks and automated validation as team standards. * Stakeholder Communication: Proactively surface risks - documentation gaps, structural mismatches - clearly and early to non-technical stakeholders, representing SQUER with confidence. ## Related Videos - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [Reality and Beyond: Coding a Drone Using {Unity 3D .NET} and ChatGPT AI!](https://www.wearedevelopers.com/videos/702-reality-and-beyond-coding-a-drone-using-unity-3d-net-and-chatgpt-ai) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [OLTP in the Lakehouse: Redefining Data for AI Workloads](https://www.wearedevelopers.com/videos/2038-oltp-in-the-lakehouse-redefining-data-for-ai-workloads) - [Enabling intelligent logistics automation: home-grown Industrial IoT platform at Austrian Post](https://www.wearedevelopers.com/videos/2018-enabling-intelligent-logistics-automation-home-grown-industrial-iot-platform-at-austrian-post) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [The Biggest German Tech Companies](https://www.wearedevelopers.com/magazine/424-the-biggest-german-tech-companies) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)