Director, Data Platform Engineering

Team: Data

Location: Remote in Poland

Commitment: Full-time Engagement

Workplace Type: remote

What will you do?

Lead a multidisciplinary data organization, hiring, developing, and establishing career frameworks while bridging legacy expertise with modern platform capabilities.
Collaborate closely with analytics, product, engineering, and business teams to provide the platform foundation they depend on.
Own the architecture and delivery of the enterprise data platform across cloud environments, supporting batch, streaming, structured, and unstructured data.
Define and govern enterprise-level data architecture patterns and data modeling standards, applying them pragmatically across domains.
Develop integration strategies for source systems to enhance data quality, prevent technical debt, and optimize access to reliable data.
Establish the data product model with clear ownership, SLAs, versioning, and discoverability, shifting the team’s output from pipelines to trusted, consumable data assets.
Lead the design and build of the enterprise semantic layer with canonical metrics and business definitions that serve BI tooling, AI grounding, and operational consumers from a single source of truth.
Embed governance in the engineering lifecycle through data contracts, policy-as-code, lineage automation, quality checks, and access controls with auditable versioning.
Build the data infrastructure required for AI/ML workloads, including feature pipelines, vector stores, embedding pipelines, and model-ready data products across all enterprise data assets.
 
 
 

We might be a match if you…

• Bring experience in data engineering, architecture, and data platforms, along with a track record of leading technical teams and delivering modern, cloud-based data solutions.
• Have built data products with real users in mind, including managing SLAs and incorporating continuous feedback loops.
• Have a hands-on background in semantic layer design and apply strong analytics engineering practices.
• Understand how to implement governance-as-code, including data contracts, lineage, quality, and access as core engineering artifacts.
• Have working knowledge of AI data infrastructure, such as feature stores, vector databases, and pipelines for unstructured data.
• Bring deep data modeling expertise across multiple paradigms and know how to apply the right approach depending on the context.
• Have experience leading teams with diverse skill sets across different geographies, as well as hands-on experience in a director-level role.
• Are proficient in the modern data stack, including cloud platforms (AWS, GCP, Azure), as well as data lakes and data warehouses such as Snowflake or BigQuery, and understand how tooling is evolving to support AI use cases.
• Are available to work in the afternoon hours, including occasional evening meetings due to U.S. time zone collaboration (we respect work-life balance but prioritize effective communication). 

Recruitment process:

• Send us your CV – it's the best way for us to get to know you.
• Meet Katrina, Recruitment Manager. 
• Join for a 60-minute Manager Interview with Robin, Chief Data and Analytics Officer.
• Technical Interview with Tomek and John.
• Data & Product Interview with Margot, Lead Product Manager, Data.  
• Meet on the final stage with Marcin and Alicja.
• and... welcome aboard! 👋