National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Recursion Agentic AI | Senior/Lead Data Engineer

Recursion Agentic AI
5 months ago
Applications closed

Who we Are


Do not wait to apply after reading this description a high application volume is expected for this opportunity.

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.

National AI Awards 2025

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.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.