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

Apply Now

Datawarehouse Lead (ERP, Informatica, Azure, ETL, SQL, BI)

Dudley
6 months ago
Applications closed

Related Jobs

View all jobs

▷ [Urgent] Lead Data Analyst | Commodities & Energy Trading - Front office | Up to £115k + Bonus, Benefits |......

Lead Data Engineer

Pre Sales Lead - Data Transformation

Pre Sales Lead - Data Transformation

Lead Data Engineer

Lead Data Engineer

Job Title: Data Warehouse Manager
Location: Dudley, West Midlands - Hybrid Working (ideally 2 days a week onsite, rest remote)
Job Type: Full time, Permanent
Salary: £70k - £90K Base Per Annum DOE, Plus Standard Company Benefits (Pension etc)

Our leading, Midlands based manufacturing client is seeking a hands on, technical Data Warehouse Lead/Manager with ERP and Azure Cloud experience, to oversee the design, development and maintenance of their data hub, as part of their corporate data warehouse solutions.

As well as being responsible for the design and development of the data platform, this is also a hands on role - 60% hands on development with 40% Team Leading including work allocation, pastoral care. The Datawarehouse Manager will have 2 people in the US to lead, along with a BI Analyst.

Responsibilities:

Designing, building, testing, and documenting ETL/ELT solutions.
Ensuring up-to-date and accurate documentation, including lineage, for all production solutions.
Monitoring and optimising the performance of data warehouse systems.
Managing data models, schemas, and metadata repositories.
Maintaining operational data warehouse builds and resolving issues promptly.
Ensuring adherence to agreed standards and controls for data marts and operational data stores.
Leading the release and promotion of new solutions to enhance functionality and productivity.Requirements:

Experience designing, writing, editing, debugging and testing advanced SQL code, stored procedures and database schemas for Microsoft SQL Server and ideally Oracle as well.
Data warehousing, data modelling, insights creation, data science, cloud solutions and data management.
ETL development and orchestration experience using Azure Data Factory and Informatica.
Experience using both Cloud (Azure) and On-prem data platform configurations.
Working within an end-to-end BI lifecycle.
Experience with development using the Microsoft Fabric suite of tools is preferred.
Knowledge and experience of working with ERP systems - essential.
Experience of working with ERP systems within the manufacturing industry a big plus.
Team Leading/Management experience.If this opportunity appeals to you and aligns closely to your background - please submit your application to Jackie Dean at Jumar for consideration.

Jumar takes great pride in representing socially responsible clients who not only prioritise diversity and inclusion but also actively combat social inequality. Together, we have the power to make a profound impact on fostering a more equitable and inclusive society. By working with us, you become part of a movement dedicated to promoting a diverse and inclusive workforce

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.