Lead Data Engineer & Tech Lead for Scalable Pipelines

Jobtailor; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Jobtailor in the United Kingdom seeks a senior data engineering leader to own and optimize our data platform. You will shape the direction of a growing team, coach members on best practices, and ensure reliable, scalable data pipelines that power business insights.

Collaborate with senior leadership, build new data sources, deploy ML infrastructure, and drive governance and data quality across streaming and batch workloads.

Qualifications

• Experience leading data engineering teams and mentoring engineers.
• Ability to own problems end‑to‑end in ambiguous contexts.
• Commitment to high‑quality code and lean processes.
• Experience with distributed data processing and data quality monitoring.
• Familiarity with data warehousing and API integration.

Responsibilities

• Own and optimise platform architecture for reliability and scalability.
• Shape team direction and coach members on best practices.
• Deliver data pipelines and new data sources, including API integrations.
• Interact with Senior Leadership to present data projects.
• Develop and deploy ML infrastructure for AI initiatives.
• Collaborate with dev teams to integrate data platform with back‑end databases.
• Scope and deliver data engineering projects with stakeholders.
• Build data sources and pipelines with third‑party integrations.

Skills

Data pipeline development

Team management and coaching

Distributed data processing

Python programming

AWS infrastructure

Tools

Databricks

Airflow

Dbt

Kubernetes

Spark

Job description

Jobtailor in the United Kingdom seeks a senior data engineering leader to own and optimize our data platform. You will shape the direction of a growing team, coach members on best practices, and ensure reliable, scalable data pipelines that power business insights.

Collaborate with senior leadership, build new data sources, deploy ML infrastructure, and drive governance and data quality across streaming and batch workloads.