Genesis
Data Engineer (Promova)
Remote Data Engineering role with clear candidate location fit.
PostedJul 3, 2026
Eligible countries1 accepted country
Seniority signalMiddle
Work settingRemote
Accepted candidate locations
Ukraine
Role overview
Data Engineer (Promova)
Requirements and responsibilities
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Details
- Direct Business Impact: The data platform you build is the infrastructure the entire company runs on. Every team — marketing, product, etc — depends on the reliability and quality of what you ship. This is foundational work that scales with the business.
- AI-First Culture: You won't be figuring out AI alone. We have a dedicated squad focused on AI adoption and a full setup ready to use: Claude Desktop connected to Tableau, Notion, and our internal data sources. You'll be building the data layer that makes AI agents actually useful.
- Multiproduct Ecosystem: As Promova expands into new products, you'll integrate new data sources and adapt pipelines — building once in a way that scales across the whole ecosystem from day one.
Core experience:
- 2+ years of hands-on experience as a Data Engineer or in a similar data infrastructure role.
- Strong SQL proficiency and practical experience working with cloud data warehouses (BigQuery preferred).
- Experience designing and maintaining ETL/ELT pipelines and layered data warehouse architecture (raw → stage → final).
- Hands-on experience with pipeline orchestration tools (Airflow, MWAA, or Cloud Functions): task dependencies, error handling, retry logic, failure recovery.
- Solid understanding of data quality practices: validation, NULL/duplicate handling, freshness and consistency monitoring.
Bonus points:
- Experience in subscription-based products (web and/or mobile).
What You Will Do
- Develop and optimize the data warehouse (BigQuery): design and maintain the raw → stage → final layer architecture.
- Connect and integrate new data sources: product, payment, marketing, internal services — into a unified pipeline.
- Orchestrate and ensure pipeline reliability: manage task dependencies, error handling, retry logic, and recovery from failures.
- Maintain data quality: build validation checks, handle NULLs and duplicates, monitor freshness and consistency.
- Prepare data as a product for AI agents: clean, documented, predictable datasets that can be trusted without manual verification.
- Contribute to analytical tasks where automation falls short, and support the team through the transition to self-service analytics.
Interview Process
- Intro-call with Recruiter
- Meeting with the Hiring Manager + Test Case
- Bar-raising
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