Tech Lead / Lead Data Engineer - Outside IR35 - SC + NPPV3 Cleared

Newington, Greater London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior/Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Data Engineer

Data Engineering Manager

Tech Lead / Lead Data Engineer (AWS Data Platform)
Rate: £500 - £550 p/d outside IR35
Length: 1st April to end of November (initially)
Location: London (hybrid – typically 1 day per week on-site, remaining remote)
Security Clearance: SC Clearance essential + NPPV3

Overview
We’re looking for a hands-on Tech Lead to lead a small team delivering secure, scalable data solutions within a highly regulated environment. You’ll take technical ownership across an AWS-based data platform using S3, Glue, and Redshift, working closely with delivery leadership, architecture stakeholders, and product teams to deliver incremental value.

This role suits someone who can balance technical leadership, hands-on engineering, and stakeholder-facing communication, while maintaining strong standards around security, quality, and operational resilience.

Key Responsibilities
Lead and mentor a small engineering team across data engineering, analytics engineering, and DevOps.
Own the technical design of data ingestion, transformation, storage, and access patterns.
Drive engineering standards including code quality, testing, CI/CD, Infrastructure as Code, and security-by-design.
Translate high-level requirements into solution increments, technical designs, and well-scoped delivery tickets.
Deliver and optimise data modelling approaches (e.g., star/snowflake schemas) and performance tuning practices.
Build reliable and cost-effective ETL/ELT pipelines, including orchestration and event-driven patterns where appropriate.
Partner with security stakeholders to ensure compliance, including IAM least privilege, encryption, auditability, and secure access controls.
Implement and maintain CI/CD pipelines for data workflows and platform components.
Ensure strong monitoring and operational discipline using cloud-native tooling and engineering best practice.
Communicate technical decisions, trade-offs, risks, and delivery progress to senior stakeholders.
Promote a culture of learning, quality, and continuous improvement.Required Skills & Experience
Proven experience as a Tech Lead / Lead Data Engineer delivering AWS-based data platforms.
Strong hands-on AWS experience, including:

Amazon S3 (data lake patterns, partitioning, lifecycle policies, cost optimisation)
AWS Glue (Jobs, Crawlers, PySpark, Glue Data Catalog, orchestration)
Amazon Redshift (performance tuning, sort/dist keys, Spectrum, WLM)
Strong development skills across:

Python (including PySpark)
SQL (DDL/DML, analytical queries, data performance considerations)
Experience with Infrastructure as Code (Terraform or CloudFormation).
CI/CD experience using tools such as GitHub Actions, Azure DevOps, CodePipeline, CodeBuild, etc.
Strong understanding of security & governance in regulated environments:

IAM, KMS encryption, Secrets Manager/SSM, audit logging
Delivery capability across Agile (Scrum/Kanban) environments with strong backlog refinement discipline.
Confident stakeholder management with the ability to explain technical choices and gain consensus

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.