Senior/Lead Data Engineer

Recursion Agentic AI
11 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Infosys - Senior Lead Analyst - Data Scientist - London, UK

Infosys - Senior Lead Analyst - Data Scientist - London, UK

Data Engineer

Senior Data Engineering Lead - Cloud Pipelines & Governance

Senior/Lead Health Data Scientist – Statistical Genetics

Who we Are

Recursion is an institutionally-backed startup currently in beta mode that is redefining business intelligence and automation with agentic AI. Our mission is to deliver non-obvious insights tailored specifically to each business and to automate complex processes beyond the capabilities of traditional RPA tools. By consolidating all data into a single source of truth and making it accessible in real-time via natural language conversations, we empower enterprises to make quick, confident decisions without delays in data processing or preparation. 


About the Role

The Data Engineer will design and maintain ETL pipelines for our clients, ensuring efficient ingestion, transformation, and storage of data. This person will play a key role in transforming various client’s data in the format most suitable for AI agents.


Key Responsibilities:

  • Design, develop, and maintain scalable ETL pipelines to ingest, transform, and store data tailored to individual client requirements.
  • Implement efficient data processing workflows for structured and unstructured data.
  • Develop processes for raw data ingestion, transformation, and storage.
  • Automate the generation of master data views on a regular basis.
  • Collaborate with data analysts and app development teams for seamless data flow.
  • Monitor and optimize data pipeline performance.


Qualifications:

  • A minimum of 5 years of experience in a professional data engineering role.
  • Expertise in ETL tools and frameworks (e.g., Apache Airflow, AWS Glue).
  • Proficiency in Python, SQL, and cloud services (AWS, GCP, or Azure).
  • Familiarity with data warehousing and transformation techniques.
  • Strong debugging and performance optimization skills.
  • Experience with real-time data processing frameworks.
  • Knowledge of machine learning pipelines and integration.
  • Familiarity with data visualization tools (e.g., Tableau, Looker, or Power BI).
  • Background in working with APIs and integrating external data sources.
  • Ownership: Track record of driving and delivering complete, high quality solutions to problems independently.
  • Experience mentoring junior team members.


How to Apply

Please submit your resume and a brief cover letter explaining your interest in the role and how your experience aligns with the responsibilities and qualifications.

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.

Education Jobs

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Jobs

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.

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.

🍪 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.