Senior Data Engineer (Contract) at Codurance

Manchester, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

21 Sep, 26

Salary

0.0

Posted On

23 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Databricks, Delta Lake, Unity Catalog, Apache Spark, dbt, MLflow, CI/CD, Infrastructure as Code, AWS, Azure, Python, Data Modeling, Lakehouse Architecture, Medallion Design, Software Engineering, Data Pipelines

Industry

IT Services and IT Consulting

Description

Codurance is a global software consultancy, focused on raising the bar in the software industry through supporting tech communities and working with our clients. We help our clients build remarkable digital products, evolve strategic legacy systems, and equip them to continuously deliver value through the adoption of quality practices and expertise. We are looking for a Contract Senior Data Engineer who shares our values of craftsmanship, pragmatism, and professionalism. You understand that data engineering is, at its core, great software engineering, applied at scale, with rigour, and with production in mind. Databricks is our primary data engineering platform, and you will be expected to use it fluently, but the goal is always durable, well-crafted data systems, not tool advocacy. This is an initial 6-month contract, inside IR35. What You’ll Work On Our data engineering engagements fall into three primary shapes: Data Foundations Data Modernisation, re-platforming legacy ETL, introducing automated testing and data contracts, and aligning data models with application domains AI-Ready Data Enablement, preparing data systems to safely support AI and ML use cases, including feature pipelines, scalable compute, and guardrails around data quality and lineage These engagements sit within wider software modernisation and platform evolution work, not as standalone data projects. You’ll work alongside software craftspeople, architects, and client engineers, with data as one part of a broader delivery picture. You consider yourself a data engineer with strong software engineering instincts. You care deeply about the quality, testability, and operability of what you build, not just whether it runs. You are comfortable working in small increments, tightening feedback loops, and letting tests inform your pipeline design. You understand that data systems live in production long after they are first delivered, and you build with that in mind. You are a team player who values frequent collaboration with software engineers, data scientists, business analysts and client stakeholders alike. You invest in your craft, stay curious about emerging tooling, and are enthusiastic about sharing what you learn. Data Engineering Practices You have solid experience applying software engineering disciplines to data: writing testable pipelines, using version control rigorously, and treating data models as first-class software artefacts. You are familiar with data contracts, schema evolution, and the principles of incremental delivery applied to data work. Platform and Architecture You have hands-on experience with Databricks and are comfortable across its core capabilities: Delta Lake, Unity Catalog, Databricks Workflows, and notebook-to-production pipeline patterns. You have a working understanding of lakehouse architecture and medallion design, and can articulate trade-offs clearly to clients and colleagues. Beyond Databricks, experience with the broader data engineering landscape, including tools such as dbt, Apache Spark, and data quality frameworks, is a plus. Data Engineering for ML and AI You understand how machine learning and AI systems consume data, and you build the pipelines that feed them to production. You are comfortable with engineering reproducible feature pipelines, managing feature tables in Unity Catalog, and using MLflow for experiment tracking and model registry so that training data and model lineage stay traceable. You prepare and version the datasets that models depend on, and you treat that data with the same rigour you apply to any production system. You are familiar with the data engineering behind modern AI use cases: building embedding and retrieval pipelines, populating and maintaining vector stores, and assembling the evaluation and ground-truth datasets that make model behaviour measurable. Software Engineering Foundations Your data engineering work is grounded in solid software fundamentals. You understand clean code principles, meaningful abstraction, and the difference between code that works and code that can be maintained. You are familiar with CI/CD applied to data pipelines and are comfortable contributing to infrastructure-as-code environments. Observability and Production Readiness You understand what it means to run data systems in production. This includes pipeline monitoring, alerting, data quality validation, lineage tracking, and the operational disciplines needed to maintain trust in data over time. Cloud Platforms You have experience working on at least one major cloud provider (AWS or Azure) and are comfortable with the infrastructure patterns that underpin modern data platforms. Familiarity with cloud-native services for storage, compute, and orchestration is expected. Collaboration and Client Engagement You can work effectively within a client’s environment and communicate clearly with both technical and non-technical stakeholders. You understand how to manage expectations, navigate legacy constraints, and make pragmatic decisions under real-world delivery conditions. Legal / Eligibility You must be eligible to work in the UK.

Responsibilities

Design and implement durable data systems focusing on data foundations, modernization, and AI-ready data enablement. Collaborate with software engineers and stakeholders to build scalable, testable pipelines within a broader software modernization context.