About the profile

We’re looking for a Data Engineering Team Lead to join our growing Data and AI Engineering practice - a role designed for someone who thrives at the intersection of technical excellence and people leadership. This is not about being the most senior engineer in the room; it’s about being the person who makes the whole team better.

You will guide a team of data engineers working on high-impact projects in the life sciences and healthcare industries, define data architecture standards, and collaborate with stakeholders to explore and shape new use cases. You bring enough technical depth to earn the team’s trust, and enough leadership instinct to develop their potential.

Responsibilities - Team Leadership & Development:

Lead, mentor, and support a team of data engineers, fostering a culture of quality, ownership, and continuous learning.
Conduct regular 1:1s, provide technical guidance, and support the career development of your direct reports.
Act as the first point of escalation for technical challenges and unblock the team when needed.
Collaborate with interdisciplinary teams, including data architects, clinical SMEs, and business stakeholders.

Responsibilities - Technical Leadership & Architecture:

Define and drive data architecture standards, design patterns, and engineering best practices across the team.
Lead architectural reviews and provide hands-on guidance on platform design decisions.
Build and oversee scalable data pipelines using Databricks, dbt, PySpark, and SQL on cloud environments (Azure, AWS, GCP).
Execute and oversee data harmonization processes on clinical trial datasets to ensure consistency, quality, and interoperability.
Develop and implement complex data processing and integration strategies to derive actionable insights that support key stakeholder decision-making.
Champion DataOps practices: CI/CD, pipeline versioning, testing, lineage, observability, and deployment automation.

Responsibilities - Stakeholder & Strategy:

Partner with clients and internal stakeholders to explore new use cases and translate business requirements into technical solutions.
Contribute to project scoping, effort estimation, and roadmap planning.
Represent the data engineering function in cross-functional discussions and technical forums.

What We’re Looking For:

We value diverse backgrounds and leadership styles. Even if you don’t check every box, we’d still love to hear from you.

Requirements - Must have

A proven track record of end-to-end ownership of enterprise-scale data solutions - from architecture and design through implementation, production deployment, and operational handover.
Demonstrated experience in a lead or senior engineering capacity, recognized for the impact delivered on team performance, solution quality, and stakeholder outcomes - not simply tenure.
Solid experience working within the life sciences industry (e.g., clinical trials, regulatory submissions, pharmacovigilance, R&D data, or commercial operations), with a strong grasp of the domain’s data complexity, standards, and compliance requirements.
Proven ability to lead, mentor, and grow a team of engineers in a collaborative environment, including conducting performance reviews, setting individual goals, and following up on personal development plans.
Solid cloud data engineering background, comfortable working across major cloud platforms (AWS, GCP, Azure) with a preference for candidates with strong Azure and Databricks experience, including Unity Catalog, PySpark, Python, and SQL.
Demonstrated experience processing and engineering large-scale datasets in production environments, including responsibility for performance monitoring, observability, alerting, and operational reliability of data systems.
Experience defining data architecture, platform standards, and engineering best practices at a team or project level.
Solid understanding of DataOps concepts: reproducibility, automation, CI/CD, and collaboration.
Exceptional problem-solving skills, critical thinking abilities, and meticulous attention to detail.
Excellent verbal and written communication skills for conveying complex data findings and architectural decisions to both technical and non-technical audiences.
A results-oriented attitude with a strong sense of ownership, openness to challenges, and a commitment to continuous improvement.

Requirements - Nice to have

Infrastructure automation using Terraform, Bash, or PowerShell.
Experience with Managed Tables, Lakehouse best practices, and Databricks Asset Bundles (DABs).
Familiarity with GxP or other healthcare data regulations.
Experience facilitating Agile ceremonies (Scrum or Kanban) as a tech lead or engineering manager.
Experience with non-relational data systems (e.g., MongoDB, CosmosDB).
Experience enabling AI and machine learning use cases through data — building the data foundations, pipelines, and feature stores that power AI-driven solutions in a life sciences or enterprise context.

Seniority Level

Lead.

Languages

English: B2+ / C1 level required.
Spanish and/or Catalan language skills are a plus.

What we offer

Hybrid work model and flexible working schedule that would suit night owls and early birds.
25 days of annual leave.
Opportunities for career development and the opportunity to shape the company's future.
An employee-centric culture directly inspired by employee feedback - your voice is heard, and your perspective encouraged.
Different training programs to support your personal and professional development.
Work in a fast-growing, international company.
Friendly atmosphere and supportive Management team.

Data Engineering Team Lead

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