[Job-32197] Tech Lead Data - Databricks

About the Role

We are looking for a senior, hands-on Data Tech Lead to drive the technical leadership of our data engineering initiatives on Databricks in Azure.

You will partner directly with the Data Architect to define standards and references, remove roadblocks, and enable the Data Engineering team to deliver scalable, reliable pipelines aligned with the business within the Medallion architecture.

Key Responsibilities

• Provide technical leadership to Data Engineering squads, supporting design, reviews, and decision-making to unblock delivery.
• Co-define with the Data Architect the technical vision, coding standards, modeling conventions, and best practices for the Lakehouse (Delta Lake + Medallion).
• Design and oversee pipelines in Databricks (PySpark/Spark SQL) across Bronze/Silver/Gold layers, ensuring security, performance, and governance.
• Implement and reinforce DataOps practices: version control (Git), CI/CD, testing (unit/integration/data quality), and documentation.
• Optimize jobs/pipelines: partitioning, Spark tuning, cost management, and reliability (baseline observability and alerting).
• Ensure non-functional requirements: security, access control, and data policies in Azure/Databricks aligned with defined governance.
• Collaborate with stakeholders (business/analytics) to translate requirements into scalable solutions with realistic delivery plans.
• Mentor engineers, promote standards, and create references (templates, playbooks) to accelerate the team.

Required Skills and Qualifications

Must-have Skills

• Strong experience in Data Engineering and projects, with experience as a Tech Lead/Senior leading teams technically and operationally.
• Strong hands-on experience in Databricks: PySpark and Spark SQL; batch pipelines (and working knowledge of streaming when needed).
• Mastery of Delta Lake and practical application of the Medallion architecture (Bronze/Silver/Gold).
• Solid experience with Azure for data, including at least: Azure Databricks, Azure Data Lake Storage (ADLS), and integration with security/identity services (e.g., Azure Active Directory).
• Strong SQL and Python skills; fundamentals in modeling (dimensional/consumption) and good practices for contracts/schemas.
• Experience with DataOps: Git, CI/CD for data pipelines, automated testing, and clear documentation.
• Proven ability to review design/code, resolve performance/scale issues, and guide teams.
• Advanced English to work with stakeholders and produce technical documentation.

Nice-to-have Skills

• Unity Catalog (governance/lineage/access control) and Databricks Workflows/Repos; Delta Live Tables.
• Data quality frameworks (Great Expectations/Soda) and/or dbt on the Lakehouse.
• Orchestration (Airflow/Databricks Workflows) and observability (metrics, logs, alerts).
• Experience with CDC/streaming ingestion (Event Hubs/Kafka) when applicable.
• Consulting experience and international client exposure; Databricks/Azure certifications.