Data Lead - Data Engineering & Data Science

About the role

Promtior, an Agentic AI company offering AI product delivery, AI department-as-a-service and AI adoption consulting, is looking for a Data Lead to head its Data team within Engineering. This is a hands-on leadership role: you will set the technical direction for data engineering and data science work across client projects, grow the team and stay close enough to the code to review designs and unblock production issues.

Key responsibilities

• Lead the Data team (data engineers and data scientists), defining its technical vision, standards and ways of working across client engagements.
• Own the architecture of the data platforms designed and delivered, from ingestion and Lakehouse/Warehouse design (Databricks, Snowflake or equivalent) to the curated datasets feeding analytics, reporting and AI/ML use cases.
• Set engineering standards for data modeling, testing, CI/CD, documentation, data quality, observability and governance, and ensure they hold in production.
• Make and defend pragmatic architectural decisions, explaining trade-offs to technical and business audiences.
• Partner with clients, project managers and AI/ML teams, take part in pre-sales and discovery, and oversee delivery quality.
• Lead resolution of critical production incidents and drive root cause analysis.
• Hire, mentor and develop the team through code and design reviews, and help shape the use of AI tools to accelerate data work.

Required profile

• 5+ years in data engineering, data architecture or data science, including at least 3 years leading a data team or setting technical direction for others.
• Proven track record designing and operating data platforms in production, end to end.
• Hands-on experience with at least one major cloud provider (AWS, Azure or GCP).
• Ability to communicate architecture, trade-offs and risks to clients and non-technical stakeholders; advanced English level.
• Especially valued: consulting experience, building a data platform or team from scratch, streaming and MLOps experience, FinOps, regulated environments, and direct work with U.S. clients.

Required skills

• Databricks or Snowflake and Lakehouse/Medallion architectures.
• Advanced Python and SQL; relational and analytical data modeling and data warehousing.
• ETL/ELT and incremental pipelines with Airflow, dbt or similar.
• Data quality, lineage, observability, CI/CD and testing standards.
• Working knowledge of the ML lifecycle and data for AI/ML or LLM use cases.
• Experience hiring, mentoring and reviewing data engineers and data scientists.

What we offer

• 20 paid days off per year.
• Hybrid work model.
• Flex days for monthly team activities.
• Professional development courses.
• Internal clubs (soccer, padel, running, cinema).