Team Lead - Data Engineering

DRW
London
2 days ago
Create job alert

DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk.

Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets.

We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus.

The Data Experience team is an important part of DRW’s Unified Platform (UP) organization, providing common data engineering capabilities and centralized storage and management for vendor data products used across the firm.

As a Team Lead of our Data Engineering team, you will lead a team of Data Engineers who handle the technical work in our firmwide data onboarding process, rapidly ingesting, curating and delivering data for Traders, Quantitative Researchers, and Back-Office business units, consulting closely with individuals to best utilize the firm’s data and platform tools. This role encompasses people leadership, technical leadership, project ownership, and hands-on development and support activities.

Key Responsibilities

  • Lead and mentor a team of data engineers with a variety of skill sets, fostering a culture of excellence, innovation, and continuous improvement.
  • Use strong leadership skills in mentoring, coaching, feedback, development, negotiation, and conflict management to enhance team performance and help people have a great experience in their work life.
  • Coordinate closely with Data Strategists to efficiently prioritize, deliver, and support datasets and data products across the firm.
  • Engage in hands-on software development and support activities, including pair programming.
  • Drive project ownership, including defining requirements, scheduling, resource allocation, and ensuring timely delivery of projects.
  • Manage a high-velocity backlog of one-off user requests and larger data onboarding projects, balancing work estimates and throughput to meet business targets.
  • Engage with stakeholders to understand how to best deploy the data experience team.
  • Contribute to process discussions, challenging ideas and actively refining the team’s processes to maximize throughput.

Required Qualifications

  • 3+ years of experience leading engineers in a technical environment, with a strong emphasis on mentoring, development, and team management.
  • 5+ years of experience working with modern data technologies and/or building data-first products.
  • Familiarity with the data modeling practices, storage systems, and compute frameworks common to modern data engineering, with a track record of leveraging this knowledge within a fast-moving data ecosystem.
  • Strong communication and interpersonal skills, capable of balancing and negotiating requests across multiple stakeholders.

Desirable Qualifications

  • Experience managing technical roadmaps across projects with multiple milestones.
  • History of working in close coordination with Product Managers, translating technical roadblocks, refining processes, and improving delivery.
  • Ability to own projects, define requirements, and lead development and support initiatives.
  • Experience supporting a large portfolio of data pipelines, rapidly delivering new workflows, and modeling new datasets to maintain consistency across the ecosystem.
  • Track record of data governance and data stewardship at scale.
  • Experience with alternative data management, acquisition (purchasing, scraping), modeling, or analysis.
  • Track record of fostering collaboration and effective teamwork across global locations. 

For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice.

California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice.

[]

Related Jobs

View all jobs

Data Engineering Team Lead - Remote - Databricks - Azure - 80k

Data Engineering - £70,000 - Hybrid

Lead Data Architect

Data Engineering Lead – Elite HFT Firm - Trading Systems - WFH - London - Up to £600k TC

Head of Data Engineering – Azure Lakehouse Lead

Alpha Data Services, Performance Ready Data Analyst, EMEA Lead, Vice President

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.

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

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.

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.