Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Senior Data Engineer - Azure

London
4 days ago
Create job alert

Senior Databricks Data Engineer

2-3 days on-site - central London

up to £75k + 20% Bonus + Excellent Benefits

Our client is a leading global Retail/FMCG brand undergoing an exciting period of rapid growth and transformation. With significant investment in data and technology, they are building a world-class data platform to power decision-making across every area of the business - from supply chain and logistics to marketing, customer sales and in-store operations.

We are seeking an experienced Senior Data Engineer with deep expertise in Databricks and Medallion Architecture (Bronze-Silver-Gold) to design, build, and optimize our next-generation data platform. This role will be pivotal in developing scalable data pipelines, enabling advanced analytics, and driving data quality and governance across the organization.

You'll work closely with data scientists, analysts, and business stakeholders to transform raw data into trusted, actionable insights that power critical business decisions.

Key Responsibilities

Design and implement scalable data pipelines and ETL/ELT workflows in Databricks using PySpark, SQL, and Delta Lake.
Architect and manage the Medallion (Bronze, Silver, Gold) data architecture for optimal data organization, transformation, and consumption.
Develop and maintain data models, schemas, and data quality frameworks across multiple domains.
Integrate data from a variety of structured and unstructured sources, including APIs, relational databases, and streaming data.
Optimize performance, scalability, and cost efficiency of Databricks clusters and workflows.
Collaborate with cross-functional teams to support analytics, machine learning, and business intelligence use cases.
Implement data governance, lineage, and observability best practices using tools such as Unity Catalog, DataHub, or Collibra.
Mentor junior engineers, fostering best practices in data engineering, testing, and DevOps for data (DataOps).
Stay current with emerging technologies in cloud data platforms, Lakehouse architecture, and data engineering frameworks.Required Qualifications

6+ years of experience in data engineering
3+ years of hands-on experience with Databricks, Delta Lake, and Spark (PySpark preferred).
Proven track record implementing Medallion Architecture (Bronze, Silver, Gold layers) in production environments.
Strong knowledge of data modeling, ETL/ELT design, and data lakehouse concepts.
Proficiency in Python, SQL, and Spark optimization techniques.
Experience working with cloud data platforms such as Azure Data Lake, AWS S3, or GCP BigQuery.
Strong understanding of data quality frameworks, testing, and CI/CD pipelines for data workflows.
Excellent communication skills and ability to collaborate across teams.Preferred Qualifications

Experience with Databricks Unity Catalog and Delta Live Tables.
Familiarity with streaming frameworks (Structured Streaming, Kafka, etc.).
Background in data observability and metadata management.
Exposure to machine learning pipelines or MLflow within Databricks.
Knowledge of infrastructure as code (IaC) using Terraform or similar tools. To apply for this role please email across your CV ASAP

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer

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.

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.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.