Staff Data Engineer (Databricks Specialist)
J
JobgetherData engineering
Listing location: Germany; Workplace type: Remote; Structured job location: GermanyFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 12+ years of professional experience in data engineering or software engineering
- Required Skills
- AWSGraphQLPythonSQLGCPAzureRESTful APIsDatabricks
Requirements
- Bring 12+ years of professional experience in data engineering or software engineering.
- Have a track record of architecting enterprise-scale data platforms and pipelines.
- Demonstrate expert-level SQL skills and strong proficiency in Python; Scala or Java experience is an advantage.
- Have extensive hands-on Databricks experience and experience with cloud data platforms such as Snowflake, Azure Synapse, BigQuery, or Amazon Redshift.
- Understand cloud-native architecture, distributed systems, data modeling methodologies, and ETL/ELT architectures.
- Have experience designing enterprise MDM solutions, including entity resolution, match-and-merge, golden records, reference data, and hierarchy management.
- Have practical experience building or integrating agentic AI systems, LLM applications, RAG pipelines, and AI agent orchestration frameworks.
- Have experience developing backend data services and REST or GraphQL APIs.
- Understand relational and NoSQL databases and data lake and lakehouse formats such as Delta Lake, Apache Iceberg, and Parquet.
- Understand CI/CD, Infrastructure as Code, DevOps, and DataOps practices.
- Demonstrate ability to lead large-scale technical initiatives across teams and stakeholders.
- Have mentoring and coaching experience; a related bachelor's degree is preferred.
Responsibilities
- Own the technical architecture of cross-functional data initiatives spanning ingestion, transformation, storage, and serving.
- Design and maintain scalable data pipelines and distributed platforms using Databricks and cloud technologies across Azure, AWS, or GCP.
- Lead enterprise MDM initiatives covering golden records, entity resolution, match-and-merge, reference data, hierarchies, and stewardship.
- Integrate agentic AI and LLM-powered solutions, including autonomous agents, RAG pipelines, copilots, and orchestration frameworks.
- Build data foundations for machine learning and AI, including feature stores, vector databases, embeddings, and ML/LLMOps pipelines.
- Deliver backend data services and REST or GraphQL APIs for applications, analytics, and business intelligence platforms.
- Establish engineering standards for data modeling, quality, testing, observability, CI/CD, deployment, and responsible AI-assisted development.
- Implement data governance, lineage, cataloging, and quality frameworks.
- Lead technical design reviews, provide architectural guidance, and influence decisions across teams.
- Mentor and coach engineers and address platform performance, reliability, and scalability challenges.
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