Senior Data Engineer

Overview

A growing technology consultancy is hiring a Senior Data Engineer on a permanent, full-time basis. The role is predominantly remote, with ad hoc travel to client sites roughly two to four times a month. There is a London office available for anyone who prefers a base to work from. Salary is £80,000 to £100,000 depending on experience.

This hire is driven by live client demand across multiple active engagements, so the person stepping into this role will be making a real impact from day one. The work is varied by design: you will move across clients, stacks, and problem spaces, building data platforms that genuinely change how organisations use their data. The consultancy is also actively developing its AI offering, and this person will have the chance to shape that from the inside. If you are a data engineer who finds energy in variety, values autonomy, and wants to grow beyond pure delivery into practice-building and client partnership, this is a strong opportunity to do that.

Key Responsibilities

• Design, build, and maintain scalable, reliable cloud-native data pipelines and platforms for client engagements
• Work across the modern data stack, adapting to client environments and tech choices
• Apply strong software engineering fundamentals: unit testing, CI/CD, Git branching strategies, and environment management
• Build and run scheduled ETL/ELT pipelines including monitoring, error handling, and orchestration
• Design and implement cloud solutions, justifying service choices with reference to cost, scale, reliability, security, and observability
• Implement dimensional and analytics-ready schemas and explain trade-offs clearly
• Manage and communicate with client stakeholders, setting and meeting expectations throughout engagements
• Contribute to the internal data engineering practice and the business's growing AI offering
• Work in agile scrum teams across all client projects

Requirements

Must-haves

• Production-grade Python for ETL, automation, and data manipulation
• Strong SQL: joins, aggregations, window functions, performance tuning, and complex analytics queries
• Hands-on experience with at least one major cloud platform (GCP, AWS, or Azure) and its data tooling
• Pipeline and orchestration experience (Airflow, Dagster, Prefect, dbt, Kafka/Spark, or similar)
• Solid knowledge of relational databases and dimensional modelling
• Software engineering fundamentals: testing, CI/CD pipelines, Git, code reviews
• Distributed computing with Spark
• Comfortable working in ambiguity and switching context across clients and projects without losing momentum
• Strong client-facing communication skills

Nice-to-haves

• Infrastructure as code (Terraform, CloudFormation, or similar)
• MLOps experience: model deployment and serving using MLflow, Azure ML, SageMaker, or similar
• Model drift detection, performance monitoring, and automated retraining
• End-to-end ML pipeline automation and CI/CD for ML workflows
• NoSQL database experience (MongoDB, DynamoDB, or similar)

What Success Looks Like

• Strong client relationships built across engagements, with positive feedback from stakeholders
• Consistently good peer and stakeholder reviews reflecting both technical quality and collaborative working
• Active contribution to the internal data engineering practice, not just client delivery
• Visible involvement in building out the business's AI offering
• Clear growth as an engineer over the twelve-month period, asking good questions and developing expertise along the way

Team and Culture

• Agile scrum across all projects, with a genuine commitment to engineering excellence
• A consultancy environment that values autonomy: you are trusted to get on with the work
• A team that is actively building new capabilities, including AI, so there is room to shape things, not just execute them

Challenges

• Projects move fast and context shifts frequently: the person who thrives here finds that energising rather than unsettling
• Client engagements are live and demanding, so the expectation is to contribute meaningfully from early on
• The tech stack varies by client, so breadth and adaptability matter as much as depth in any single tool