At a Glance
- Tasks: Own data onboarding and build scalable, reliable data pipelines for analytics.
- Company: Join Rimes, a leader in enterprise data management solutions.
- Benefits: Enjoy 28 days annual leave, gym discounts, and flexible hybrid work.
- Other info: Diverse and inclusive workplace with excellent career growth opportunities.
- Why this job: Make an impact by transforming data into actionable insights.
- Qualifications: 1-3 years in data engineering with skills in Python, SQL, and data modeling.
The predicted salary is between 50000 - 60000 £ per year.
About Rimes
Rimes provides enterprise data management solutions to the global investment community. Driven by our passion for solving the most complex data problems, we provide our clients with investment intelligence that powers more than US$75 trillion in assets under management annually. The world's leading institutional investors, asset managers and service providers rely on Rimes to help them make better investment decisions using accurate information and industry-leading technology.
The Opportunity
We’re looking for a Data Engineer to own data onboarding and build scalable, reliable data pipelines that power analytics, operational workflows, and data‑driven decisions across Rimes. You’ll work closely with data producers, analysts, and product teams to ingest, transform, and operationalize data, primarily within Palantir Foundry (our core data platform) and complementary cloud compute. Note: Experience with Palantir Foundry is a strong plus but not required. If you bring solid data engineering fundamentals in Python/PySpark, SQL, and modern ELT patterns, we’ll support a fast ramp‑up on Foundry.
Responsibilities
- Ingest & onboard datasets from internal systems, APIs, databases, files, external providers, and real‑time feeds.
- Build and operate scalable ETL/ELT pipelines using Python, PySpark, SQL, and Foundry pipeline tooling; schedule and automate batch/stream refreshes.
- Model and operationalize data (e.g., defining entities/relationships) to support analytics and operational applications in collaboration with domain experts.
- Ensure trust in data through testing, data quality checks, observability/alerting, lineage, and compliant access controls.
- Collaborate with analysts and product teams to translate business requirements into robust data solutions and clear data contracts/SLOs.
What Success Looks Like (First 3–6 Months)
- You onboard and productionize new data sources with reliable refresh (scheduled or real‑time).
- You deliver trusted, well‑documented datasets consumed by analytics and operational teams.
- Key business entities are clearly modeled and discoverable.
- Pipelines have meaningful monitoring and alerting, with reduced failures/re‑runs.
- You contribute to standards/templates that speed up future onboarding.
Requirements
- 1-3 years in data engineering or analytics engineering with end‑to‑end pipeline delivery in production.
- Proficiency in Python & PySpark for distributed data processing.
- Strong SQL for analytical and transformation logic.
- Data modeling skills for both analytics and operational use cases.
- Experience with data ingestion from APIs, databases, external feeds, and real‑time sources.
- Solid grasp of data quality, testing, observability, lineage, and governance practices.
- Comfort working with large datasets and distributed compute using modern ELT patterns.
Nice To Have
- Palantir Foundry: pipelines/transforms, Code Repos, Ontology, and operational applications.
- Spark execution concepts: partitions, shuffles, caching, and performance optimization.
- Exposure to Databricks or cloud‑native compute with compute pushdown.
- Experience with financial or enterprise operational data.
- Experience with AI‑assisted ETL/ELT or data quality tooling.
- Familiarity with streaming frameworks and/or orchestration tools.
What We Offer
- 28 days of annual leave
- AXA Gym Membership Discount
- Healthshield Cashback plan
- Healthshield Perks platform (Breeze)
- MetLife Afterlife Support
- Metalife GP 24 hour virtual GP service
- Annual 'purchase holiday' scheme
- Chubbs Travel Insurance
Compensation: Competitive pay and bonus eligibility
Work Life Balance: Flexible hybrid work environment
Rimes is committed to promoting the values of diversity and inclusion throughout the business. We are committed to improving opportunities for people regardless of their background or circumstances.
Data Engineer employer: Rimes
Rimes is an exceptional employer that fosters a collaborative and innovative work culture, where Data Engineers can thrive in a flexible hybrid environment. With a strong commitment to employee growth, Rimes offers comprehensive benefits including 28 days of annual leave, health perks, and competitive compensation, ensuring that team members are well-supported both personally and professionally. Located at the heart of the financial sector, Rimes provides unique opportunities to work with cutting-edge technology and contribute to impactful data solutions for the global investment community.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Tip Number 1
Network like a pro! Reach out to current employees at Rimes on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the Data Engineer role. Personal connections can make a huge difference!
✨Tip Number 2
Show off your skills! Prepare a mini-project or a portfolio that highlights your data engineering expertise, especially in Python, PySpark, and SQL. This will give you something tangible to discuss during interviews and demonstrate your capabilities.
✨Tip Number 3
Practice makes perfect! Get comfortable with common interview questions related to data engineering. Focus on explaining your thought process and problem-solving skills, especially around building ETL/ELT pipelines and data quality checks.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining Rimes and being part of our mission to solve complex data problems.
We think you need these skills to ace Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Engineer role. Highlight your experience with Python, PySpark, and SQL, and don’t forget to mention any relevant projects or achievements that showcase your data engineering skills.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about data engineering and how your skills align with Rimes' mission. Be sure to mention any experience with data onboarding and pipeline building.
Showcase Your Problem-Solving Skills:Rimes loves problem solvers! In your application, share examples of how you've tackled complex data challenges in the past. This will demonstrate your ability to contribute to our mission of providing investment intelligence.
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to see all the details about the role and our company culture!
How to prepare for a job interview at Rimes
✨Know Your Data Engineering Fundamentals
Make sure you brush up on your data engineering basics, especially in Python, PySpark, and SQL. Be ready to discuss how you've used these skills in past projects, as they'll want to see your practical experience with building ETL/ELT pipelines.
✨Familiarise Yourself with Rimes' Core Values
Before the interview, take some time to understand Rimes' mission and values. Knowing how they provide enterprise data management solutions will help you align your answers with their goals and demonstrate your genuine interest in the company.
✨Prepare for Technical Questions
Expect technical questions that test your knowledge of data ingestion, transformation, and operationalisation. Practise explaining your thought process when solving data-related problems, as this will showcase your analytical skills and ability to collaborate with teams.
✨Showcase Your Problem-Solving Skills
Be ready to share specific examples of how you've tackled complex data challenges in the past. Highlight your approach to ensuring data quality and governance, as well as any experience you have with monitoring and alerting systems to reduce pipeline failures.
