Data Engineering Team Lead @
3 days ago
- 5+ years of experience in data engineering, with strong hands-on expertise in building and maintaining data pipelines;
- At least 2 years in a team leadership or technical lead role;
- Proficient in Python, SQL, and data orchestration tools such as Airflow;
- Experience with both SQL and NoSQL databases, such as MySQL, Presto, Couchbase, MemSQL, or MongoDB;
- Bachelor's degree in Computer Science, Engineering, or a related field;
- English – Upper-Intermediate or higher.
- Background in NOC or DevOps environments is a plus;
- Familiarity with PySpark is an advantage.
About Us
We are a leading Israeli IT company with 15 years of market experience and 8 years in Ukraine. Officially registered in Ukraine, Israel, and Estonia, we employ over 100 professionals worldwide. Specializing in successful startup collaboration, we offer services across e-commerce, Fintech, logistics, and healthcare.
Our client is leading mobile app company that depends on high-volume, real-time data pipelines to drive user acquisition and engagement. This role is instrumental in maintaining data reliability, supporting production workflows, and enabling operational agility across teams. This is a hands-on leadership role that requires deep technical expertise, ownership mindset, and strong collaboration across engineering and business stakeholders.
Why Join Us?
Be part of a friendly international team, working together on interesting global projects;
Enjoy many chances to grow, learn from mentors, and work on projects that make a real difference;
Join a team that loves fresh ideas and supports creativity and new solutions;
Work closely with clients, building great communication skills and learning directly from their needs;
Thrive in a workplace that values your needs, offering flexibility and a good balance between work and life.
,[Oversee daily data workflows, troubleshoot failures, and escalate critical issues to ensure smooth and reliable operations; , Use Python, SQL, and Airflow to configure workflows, extract client-specific insights, and adjust live processes as needed;, Build and maintain automated data validation and testing frameworks to ensure data reliability at scale;, Own and evolve the metadata system, maintaining table lineage, field definitions, and data usage context to support a unified knowledge platform;, Act as the primary point of contact for operational teams and stakeholders, ensuring consistent collaboration and high data quality across the organization.] Requirements: Data engineering, Data pipelines, Python, SQL, Airflow, NoSQL, MySQL, Presto, Couchbase Server, MongoDB, Degree, DevOps, PySpark-
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