Senior Data Engineer (AI-Native) — Data Layer
New
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JobgetherSaaS AI Technology
Fully remote work environment across Europe, Meaningful overlap with international teamsFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English proficiency at C1 level or above
- Experience
- 7+ years
- Required Skills
- SQLData engineeringData modeling
Requirements
- 7+ years of hands-on experience as a data engineer with proven ownership of production-scale data systems.
- Strong programming and SQL skills, with the ability to design efficient pipelines, schemas, and analytical models.
- Deep understanding of data engineering principles, including query optimization, debugging, and data correctness.
- Experience building reliable ingestion and ELT pipelines from complex and varied data sources.
- Hands-on experience with cloud data warehouses and major cloud platforms.
- Experience working with multiple data source types, including files, event streams, and APIs.
- Strong understanding of data consistency challenges such as partial loads, schema changes, late-arriving data, and idempotency.
- Experience using AI-assisted development tools such as Claude Code, Cursor, Codex, or similar solutions.
- Ability to structure AI-assisted workflows, validate outputs, and apply strong engineering judgment.
- Strong ownership mindset with the ability to move quickly and make pragmatic technical decisions.
- Excellent written and verbal communication skills.
- English proficiency at C1 level or above.
Responsibilities
- Own the Data Layer architecture, including ingestion pipelines, medallion-style data models, and serving layers.
- Design, build, and maintain reliable ingestion and transformation pipelines across various data sources.
- Integrate and reconcile complex real-world data from file-based systems, streaming events, and APIs.
- Develop data models across raw, refined, and curated layers to ensure data quality and accessibility.
- Build systems that guarantee data reliability through validation, reconciliation, lineage tracking, backfills, and incremental processing.
- Improve the scalability, performance, and tooling of the data platform as requirements evolve.
- Leverage AI-powered development tools to accelerate engineering workflows while maintaining high standards.
- Define and maintain data contracts in collaboration with backend, AI, product, and customer-facing teams.
- Troubleshoot complex data issues and ensure downstream systems receive accurate information.
- Take ownership of projects from concept through production.
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