Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineering Team Lead

Gresham Hunt
London
9 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Senior Data Analyst

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer refH225

Senior Data Scientist

Data Engineering Team Lead Financial Services
London, UK – Hybrid, 2 days/week in office
Salary: £70-75,000 + DOE
Gresham Hunt is currently partnered with a leading financial services provider who are seeking an experienced Data Engineering Team Lead to join their London-based team. This is an exciting opportunity to play a lead role in the development of the cloud migration strategy as well as maintaining the firms existing legacy on-prem database infrastructure.

Responsibilities:

  • Oversee the establishment of a framework, processes, and systems for a central data view to run and improve the business with strategy and governance.
  • Lead the migration of on-premise legacy databases to the cloud, including developing a strategy, plan, and implementation with other teams.
  • Oversee the engineering processes to build data pipelines, integrate data sources, clean and transform data.
  • Coach the team on techniques for building code to extract raw data and ensure data quality across the pipeline.
  • Provide expertise on transforming raw data for downstream data sources.
  • Guide the development of data tools for data transformation, management, and access.
  • Advise the team on writing and validating code to test data platform storage and availability for improved resilience.
  • Oversee the implementation of performance monitoring protocols across data pipelines.
  • Coach the team on building visualizations and aggregations to monitor pipeline health.
  • Implement solutions to minimize points of failure across environments.
  • Oversee the design of data modelling and handling procedures to ensure compliance with all applicable laws and policies.
  • Work with stakeholders across directorates to address data concerns.
  • Support assessment of data costs, access, usage, use cases, dependencies across products, and data availability for internal and external stakeholders.
  • Build cross-functional relationships with IT, Security, and Architecture to support data requirement delivery to business stakeholders.


The Successful Candidate will have experience in:

  • Team Leadership: Previous experience managing a team of Data Engineers and Analysts.
  • Database Management: Extensive knowledge of MS SQL databases, both Azure cloud and on-premises, including design, modelling, and architecture.
  • Cloud Migration: Experience in migrating legacy applications to the cloud.
  • Data Tools and Programming: Proficiency in data tools and programming languages like Python, DAX, R, M, VBA, and the SQL Stack (SSMS, SSIS, SSAS, SSRS).
  • Data Visualization: Experience with data visualization tools such as Power BI, Qlik, and Tableau.
  • Data Engineering and Analytics: Proven ability to establish, develop, and implement a data engineering and data analytics practice area or function.
  • Stakeholder Management: Effectively understanding and addressing stakeholder needs.
  • Graphical Development Tools: Experience with tools like Data Flows, Ab Initio, and Power Apps (desirable).


For a confidential conversation please forward your CV to:

All candidates must currently be based in the UK with full right to work.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Jobs

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Jobs

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.