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

City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineering Team Lead

Lead Analytics Engineer – DBT, Snowflake, AWS

Graduate Data Engineer

Data Engineer - Junior

Data Engineer (MS Fabric)

Senior Data Engineer

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

Join a Leading Microsoft Consultancy Driving Data Innovation

I'm working with a well-established Microsoft Partner with an incredible project pipeline, rapid growth, and a reputation for delivering Tech for Good. They're working on cutting-edge projects using emerging technologies like Microsoft Fabric and Azure Databricks.

We're looking for a Data engineering team lead who combines hands-on technical expertise with leadership skills to mentor a talented team and deliver exceptional solutions for our clients.

What You'll Do

Lead and mentor a team of Technical Consultants, driving engagement, growth, and alignment with our culture.
Oversee resource planning, scheduling, and performance management.
Collaborate with Pre-sales, Commercial, and Project Management teams to scope and deliver projects.
Ensure consistent delivery of technical solutions aligned with best practices and standards.
Support technical delivery when needed, including designing scalable data solutions in Microsoft/Azure environments.
Contribute to innovation through cloud migrations, data lakes, and robust ETL/ELT solutions.What We're Looking For

Hands-on Data Engineering experience (not Data Analyst or Data Scientist roles).
Strong background in Azure Synapse, Databricks, or Microsoft Fabric.
Expertise in ETL/ELT development using SQL and Python.
Experience implementing data lakes and medallion lakehouse architecture.
Skilled in managing large datasets and writing advanced SQL/Python queries.
Solid understanding of BI and data warehousing concepts.
Excellent communication skills and ability to build strong relationships.
Ideally, experience in consulting environments and working within high-performing teams.

The company

Rapid Growth & Exciting Projects - Continuing to grow working on cutting-edge Microsoft Cloud solutions.
Investment in You - They invest in training and development, with clear certification pathways for you.
Home-Based Contract - Work remotely with all travel expenses covered.
UK-Based Delivery - We never offshore; all consultants are UK-based.
Competitive salary and benefits, including:
25 days holiday
Private health insurance (after one year)
Life assurance (4x base salary)
Enhanced parental pay
Perkbox, Cyclescheme, Electric Car SchemeReady to lead and innovate? Apply now or send your CV directly to me

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 to Write a Data Engineering Job Ad That Attracts the Right People

Data engineering is the backbone of modern data-driven organisations. From analytics and machine learning to business intelligence and real-time platforms, data engineers build the pipelines, platforms and infrastructure that make data usable at scale. Yet many employers struggle to attract the right data engineering candidates. Job adverts often generate high application volumes, but few applicants have the practical skills needed to build and maintain production-grade data systems. At the same time, experienced data engineers skip over adverts that feel vague, unrealistic or misaligned with real-world data engineering work. In most cases, the issue is not a shortage of talent — it is the quality and clarity of the job advert. Data engineers are pragmatic, technically rigorous and highly selective. A poorly written job ad signals immature data practices and unclear expectations. A well-written one signals strong engineering culture and serious intent. This guide explains how to write a data engineering job ad that attracts the right people, improves applicant quality and positions your organisation as a credible data employer.

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

If you are applying for data engineering jobs in the UK, maths can feel like a vague requirement hiding behind phrases like “strong analytical skills”, “performance mindset” or “ability to reason about systems”. Most of the time, hiring managers are not looking for advanced theory. They want confidence with the handful of maths topics that show up in real pipelines: Rates, units & estimation (throughput, cost, latency, storage growth) Statistics for data quality & observability (distributions, percentiles, outliers, variance) Probability for streaming, sampling & approximate results (sketches like HyperLogLog++ & the logic behind false positives) Discrete maths for DAGs, partitioning & systems thinking (graphs, complexity, hashing) Optimisation intuition for SQL plans & Spark performance (joins, shuffles, partition strategy, “what is the bottleneck”) This article is written for UK job seekers targeting roles like Data Engineer, Analytics Engineer, Platform Data Engineer, Data Warehouse Engineer, Streaming Data Engineer or DataOps Engineer.

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

Every modern organisation runs on data – but without good data engineering, even the best dashboards & machine learning models are built on sand. Data engineers design the pipelines, platforms & tools that make data accurate, accessible & reliable. Those pipelines need people who can think in systems, spot patterns in messy logs, notice what others overlook & design elegant solutions to complex problems. That is exactly why data engineering can be such a strong fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & considering a data engineering career, you might have heard comments like “you’re too disorganised for engineering”, “too literal for stakeholder work” or “too distracted for complex systems”. In reality, the traits that can make traditional office environments hard often line up beautifully with data engineering work. This guide is written for data engineering job seekers in the UK. We’ll cover: What neurodiversity means in a data engineering context How ADHD, autism & dyslexia strengths map to common data engineering tasks 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 engineering – & how to turn “different thinking” into a genuine professional superpower.