Data Technical Lead

About the role

Marsh is looking for a hands-on Data Technical Lead for its Innovation & Data Office (IDO) in Dublin. You will lead a squad of 4-8 data engineers (employees and contractors) and also build things yourself, from whiteboarding data models to debugging production pipelines. The role is technical leadership, not pure people management, and you will deliver on initiatives that move from project to steady state.

Key responsibilities

• Run a delivery pod of 4-8 data engineers, set technical direction, decompose work, manage sprint execution and hold the team accountable for what it ships.
• Write code, build pipelines, debug production issues and step in personally when things break.
• Design maintainable solutions: data models, medallion architectures, ingestion contracts, schema evolution, data quality checks and lineage.
• Work with Engagement Leads and business stakeholders to turn business problems into data solutions and push back on requests that do not make technical sense.
• Drive engineering standards: CI/CD, version control, testing, code review, documentation and developer experience.
• Build reusable assets such as curated datasets, documented schemas and tested pipelines so each initiative leaves things better than it found them.

Required profile

• 5+ years of hands-on data engineering experience, including some time leading or mentoring engineers.
• Ability to lead a mixed team of employees and contractors, setting expectations and building cohesion.
• Fluency with modern engineering practices: Git, CI/CD, automated testing, infrastructure-as-code and DataOps.
• Intellectual curiosity about the business domain and clear communication with both business leaders and engineers.

Required skills

• Strong, recent use of SQL, Python and Spark.
• Cloud data platforms on AWS, Azure or GCP; Databricks strongly preferred (Snowflake, Synapse or similar accepted).
• Nice to have: Databricks (Unity Catalog, Delta Lake, medallion architecture), dbt, data governance patterns, document or unstructured data ingestion, and exposure to LLM-based tooling or analytics engineering.

The role is hybrid, with flexibility to work remotely and to collaborate in the office.