Senior Data Engineer
ago
Location
Waterloo, London
Hybrid
Salary
£80k+ per year
Experience Level
Entry
Junior
Mid
Senior
Expert
Our Client
Global Energy Company
SPECIALTY
Upstream/Downstream, Bio-Fuels, Integrated Gas, New Energies, Chemicals, Energy and Trading
INDUSTRY
Energy
Company Size
80000+ Employees
Aubay's Take
Our client is one of the Super Major global energy companies with who are working to power progress through cleaner energy solutions. You will have the opportunity to work in a challenging but rewarding environment that is fast paced and changing fundamentally, and work towards transforming the business of a Super Major energy company to meet the ambition to be a net-zero emissions energy business by 2050, whilst delivering a world class business case that has a strong societal license to operate. In your role you will be expected to enact change and deliver value globally across business lines and geographies.
Benefits from Aubay
At Aubay UK, people are at the heart of our business. We offer a competitive remunerations package which includes a range of benefits. You will receive continuous support from our dedicated team of Talent Acquisition Specialists who will support your career development and success during your assignment with our client.
25 Days Annual Leave
Work From Home Opportunities
Pension Scheme
Opportunities to Work Directly with our Client
Training Opportunities
Discount Holidays at I'Aero Chalet
Role Summary
Aubay UK is seeking a Senior Data Engineer to join a leading energy client’s team, supporting strategic data transformation initiatives across global trading functions. This role focuses on designing and building robust data foundations and end-to-end solutions to maximise value from data, working closely with IT and business stakeholders. The Senior Data Engineer will play a critical role in realising the vision and data strategy through cutting-edge cloud technologies and will be responsible for mentoring junior team members and promoting a data-driven culture across the organisation.
Required Skills and Experience:
- Strong proficiency with cloud platforms including AWS, Azure, and SAP.
- Expertise in ELT development and automation, with a deep understanding of scalable pipeline design.
- Mastery of data modelling techniques, including conceptual, logical, and physical models.
- Skilled in data ingestion, integration, and manipulation using modern tools (e.g., Data Factory, Databricks, SQL DB, synapse, Glue, Airflow, Kinesis, Redshift).
- Hands-on experience with DevOps tooling including GitHub Actions, Azure DevOps, SonarQube, and PyTest.
- Proven ability to coordinate incident and change management processes.
- Adept at communicating with both technical and non-technical stakeholders.
- Demonstrated experience in documenting solution architecture and contributing to strategic technical documentation.
Desired Skills and Experience:
- Experience in project management or running Agile/Scrum teams.
- Exposure to business planning and consolidation tools such as BPC.
- Familiarity with external partner ecosystems and cross-functional delivery models.
- Experience in writing and maintaining technical documentation using tools such as MkDocs.
- Domain knowledge in trading and supply, specifically within crude and renewables portfolios.
- Understanding of commodity data models and business applications in energy and power trading.
- Experience working in enterprise environments involving global-scale data transformation projects.
Role Responsibilities:
- Design and build scalable, end-to-end data engineering solutions using modern cloud technologies (Azure, AWS, SAP).
- Act as a subject matter expert in data engineering, providing guidance and mentoring to junior engineers.
- Collaborate with business and IT stakeholders to translate strategic goals into data architecture and solutions.
- Lead efforts to modernise data platforms, including the redesign of legacy data warehouses and migration to cloud-based infrastructure.
- Manage data ingestion, integration, and transformation workflows using tools such as Azure Data Factory, Databricks, Synapse, Glue, and Kinesis.
- Ensure high standards of data quality, performance tuning, and governance.
- Coordinate incident and problem management, ensuring timely resolution and minimal disruption.
- Present technical reports and findings to stakeholders, serving as the go-to expert on data architecture and engineering best practices.
- Support community development initiatives such as Centres of Excellence (CoE) and Communities of Practice (CoP).
- Contribute to continuous improvement and efficiency across pilot programmes and enterprise-scale rollouts.