Functional Area Lead for Data Engineering

About the role

The Functional Area Lead for Data Engineering will mentor and develop a team of data engineers, ensure adherence to quality standards, and drive the adoption of modern data technologies across the organization.

Key responsibilities

• Mentor data engineering team members, support their professional growth and performance.
• Identify training needs, organize development activities and knowledge‑sharing initiatives.
• Ensure compliance with data engineering quality standards, best practices and internal procedures.
• Participate in recruitment to build a strong data engineering team.
• Collaborate with product owners and other functional teams to enhance the Data Engineering chapter’s competencies.
• Work with vendors and partners, monitor new technologies and trends.
• Actively contribute in Agile teams to support successful delivery.
• Provide technical direction for building, implementing and supporting analytical databases and data pipelines.
• Offer expert support for testing, monitoring and rapid resolution of data processes.
• Lead the adoption of modern, innovative data engineering technologies and propose technical solutions for data architecture optimization.

Required profile

• Minimum 7 years of experience in Data Engineering, including technical leadership, mentorship or people‑leadership roles.
• University degree in Computer Science, Mathematics, Statistics, Data Science, Econometrics or Data Analytics.
• Strong knowledge of data architecture design and implementation.
• Expertise in designing analytical databases, data modeling, DWH and datamart construction and maintenance.
• Experience with ETL/ELT processes and their management.
• High‑level proficiency in Python and SQL.
• Familiarity with Big Data technologies and modern data platforms.
• Excellent English communication skills.
• Proven ability to work effectively in Agile environments and collaborate with technical and business teams.

Required skills

• Python programming
• SQL
• ETL/ELT process design and management
• Data architecture and modeling
• Data warehouse (DWH) and datamart development
• Big Data technologies (e.g., Hadoop, Spark, Kafka)
• Modern data platforms (e.g., Snowflake, Databricks, Redshift)