At a Glance
- Tasks: Design and deliver innovative data solutions using modern cloud platforms.
- Company: Join a leading consultancy with a focus on business transformation.
- Benefits: Enjoy hybrid working, 26 days holiday, and wellness support.
- Other info: Dynamic role with opportunities for professional growth and development.
- Why this job: Shape data strategies and make a real impact for clients.
- Qualifications: 4+ years in data engineering with strong Azure and SQL skills.
The predicted salary is between 60000 - 80000 € per year.
As a senior consultant in the Business Transformation team, you will design and deliver data solutions using modern cloud-based platforms and advise clients on their data landscape, shaping data strategies and roadmaps.
Key Responsibilities
- Facilitate requirements gathering workshops across business areas to agree objectives, use cases, and data needs.
- Support the development of data solutions that align initiatives to business objectives, balancing best practice ways of working with clients’ requirements.
- Provide guidance on data governance and strategies, including advising on recommended frameworks and policies to enable clients to increase their data maturity.
- Manage delivery across multiple workstreams and engagements, support junior team members, and apply structured engineering practices across the team.
- Design, build, and optimise end‑to‑end data pipelines (ingestion, transformation, orchestration) using SQL and Python, focusing on reliability, maintainability and performance.
- Participate in and lead client engagements, facilitating workshops, confirming objectives, and developing delivery plans and budgets.
Qualifications & Experience
- Strong understanding of Azure data services and integration systems (eg, Azure Data Factory, Azure Data Lake) and how they complement Fabric/Databricks.
- Experience implementing platform security practices (secure connectivity to sources, secrets management, role‑based access concepts) aligned to client policies.
- Experience in formal data quality assessments and defining reconciliation approaches with business stakeholders.
- Client‑facing data engineering experience (3+ years) in a professional services environment, including leading requirements workshops with clients.
- Hands‑on engineering capability across Microsoft Fabric and Azure Databricks, with SQL and Python experience to design and build scalable data solutions; background in PySpark desirable.
- Experience in proposal writing and business development, supporting bids and client presentations.
- Relevant certifications (desirable): Azure Data Engineer / Fabric Data Engineer or Analytics Engineer, Databricks fundamentals.
- Strong academic record; preferably a relevant degree (computer science, software engineering, data engineering, data analytics, or information systems).
- Minimum of 4 years’ work experience, ideally within a professional services environment, delivering data engineering and/or data platform engagements.
- Understanding SQL Server Databases, SQL Server Integration Services (SSIS), Azure Data Resources, Azure Data Factory, Azure Data Lake, Azure Databricks, and Azure Analysis Services.
- Strong engineering practices: version control (Git), CI/CD for data and platform artefacts, and disciplined release management via Azure DevOps (ADO).
- Experience implementing data quality controls and validation checks, and producing clear documentation to support traceability, handover and adoption.
- Good communication and presentation skills, with the ability to explain complex ideas to non‑technical stakeholders.
- Effective project management skills and the ability to meet deadlines while working on multiple projects simultaneously.
- Create high‑quality, client‑facing document outputs.
- London‑based or willing to travel to London fortnightly.
Benefits
- Hybrid and flexible working.
- 26 days holiday, with optional additional days.
- Lifestyle, health, and wellbeing support, including financial wellbeing benefits, electric car scheme, and access to a virtual GP.
- Access to a suite of 300+ on‑demand courses developed by the in‑house Talent Development team.
Data Engineering - Senior Consultant in London employer: Energy Jobline CVL
As a Senior Consultant in Data Engineering, you will thrive in a dynamic and supportive work environment that champions innovation and professional growth. Our London-based team offers hybrid working options, generous holiday allowances, and comprehensive wellbeing support, ensuring you can balance your career with personal life while continuously developing your skills through access to over 300 on-demand courses. Join us to make a meaningful impact on clients' data strategies and enjoy the unique advantages of working in one of the world's most vibrant cities.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineering - Senior Consultant in London
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the data engineering field. You never know when a casual chat could lead to your next big opportunity.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects, especially those involving SQL, Python, and Azure services. Having tangible examples of your work can really set you apart from the competition.
✨Ace the Interview
Prepare for your interviews by practising common questions and scenarios related to data engineering. Be ready to discuss your experience with Azure Data Factory and Databricks, and don’t forget to highlight your client-facing skills!
✨Apply Through Our Website
Make sure to apply directly through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Data Engineering - Senior Consultant in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Senior Consultant in Data Engineering. Highlight your experience with Azure data services and any relevant projects you've worked on that align with the job description.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're the perfect fit for this role. Share specific examples of how you've facilitated requirements gathering workshops or managed delivery across multiple workstreams, as these are key responsibilities.
Showcase Your Technical Skills:Don’t forget to mention your hands-on experience with SQL, Python, and Azure Databricks. We want to see how you’ve designed and built scalable data solutions, so be specific about your achievements!
Apply Through Our Website:We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and get you into our system quickly!
How to prepare for a job interview at Energy Jobline CVL
✨Know Your Data Tools
Make sure you brush up on your knowledge of Azure data services and integration systems like Azure Data Factory and Azure Databricks. Be ready to discuss how you've used these tools in past projects, as well as any challenges you faced and how you overcame them.
✨Showcase Your Client Engagement Skills
Since this role involves leading client engagements, prepare examples of how you've facilitated requirements gathering workshops or developed delivery plans. Highlight your ability to communicate complex ideas to non-technical stakeholders, as this will be key in demonstrating your fit for the position.
✨Demonstrate Engineering Best Practices
Be prepared to talk about your experience with version control, CI/CD processes, and disciplined release management. Discuss specific instances where you implemented data quality controls and validation checks, as this shows your commitment to maintaining high standards in your work.
✨Prepare for Scenario-Based Questions
Expect scenario-based questions that assess your problem-solving skills in real-world situations. Think about how you would approach designing and optimising end-to-end data pipelines, and be ready to explain your thought process clearly and logically.
