Data Engineer
Hybrid · Kyiv, Kyiv City, Ukraine
Job Summary
Data Engineer to design and scale cloud-native data architectures (BigQuery) and reliable ETL/ELT pipelines. Build data marts and analytical datasets for reporting and product insights; implement data quality with monitoring, logging, and alerting; optimize production data infrastructure for performance and cost. Collaborate with Data Science, product, analytics, and engineering teams to deliver scalable data solutions, support ML initiatives, and enable fast, data-driven decisions. Requires 3+ years in data engineering, strong SQL and Python skills, BigQuery expertise (partitioning, clustering, cost management), and experience with ClickHouse, GCP services, and CI/CD practices. Remote-friendly with Kyiv-based hybrid work arrangement; ideally a mid-level data engineering professional.”,
Required Qualifications
- 3+ years of experience as a Data Engineer or equivalent in large-scale data platforms
- Strong expertise in data modeling, data warehousing concepts, and analytical data architecture design
- Proven experience designing and developing complex ETL/ELT workflows
- Hands-on experience with BigQuery (partitioning, clustering, query optimization, cost management)
- Strong SQL skills for complex analytical queries
- Proficiency in Python and data libraries (Pandas, PySpark)
- Experience with ClickHouse
- Solid knowledge of Google Cloud Platform services (Cloud Storage, Pub/Sub, DataForm, IAM, service accounts)
- Experience with Git and CI/CD practices
- Understanding of monitoring, data validation, and production-grade data quality practices
- Excellent analytical thinking and communication skills
- Ability to work independently in a fast-paced environment
- Experience with BI tools like Tableau or Looker
- Experience integrating data platforms with BI tools
- 2+ years of software engineering experience
- Familiarity with ML workflows and AI-assisted engineering tools (optional)
- Delivered data solutions across product, analytics, and engineering teams
- Smile toward collaborating with Data Science teams
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