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- Tasks: Lead a team to design and maintain scalable data solutions and deliver impactful data products.
- Company: Fast-growing SaaS company with a modern, collaborative culture.
- Benefits: Competitive salary, hands-on leadership role, and opportunities for innovation.
- Why this job: Shape the future of a data platform and make a real impact on customer insights.
- Qualifications: Experience in leading data engineering teams and strong technical skills in Databricks and Python.
- Other info: Dynamic environment with a focus on learning and experimentation.
The predicted salary is between 48000 - 72000 ÂŁ per year.
We are partnering with a fast‑growing SaaS company to hire a Lead Data Engineer who will help shape and scale their next‑generation data and analytics platform. This role sits within a modern data function where engineering, analytics, and product work closely together. The Lead Data Engineer will guide a small team, own the full data lifecycle, and deliver trusted, high‑performance data products that support customers and internal stakeholders. It’s a hands‑on leadership role with strong technical influence — ideal for someone who enjoys building scalable pipelines, improving data quality, and shaping the direction of a growing platform.
What You’ll Be Doing
- Leading a small team of data engineers and analysts to design, build, and maintain scalable data solutions.
- Owning the end‑to‑end data lifecycle — from ingestion and transformation through to analytics and data product delivery.
- Architecting and operating pipelines using Databricks, Spark, and Delta Lake, ensuring performance, reliability, and cost‑efficiency.
- Working closely with BI developers and analysts to deliver dashboards, extracts, datasets, and APIs that power customer insights.
- Shaping platform architecture and setting technical direction for data engineering best practices.
- Driving improvements in data quality, lineage, governance, and observability.
- Playing a key role in data DevOps, CI/CD, testing, and cloud operations.
- Partnering with product and engineering teams to align work with the platform roadmap.
- Overseeing operational monitoring and support for the data platform.
- Promoting a learning culture in the team and encouraging experimentation with new tools and approaches.
- Mentoring team members and supporting their development.
Skills & Experience Required
- Experience leading or mentoring data engineering teams within a SaaS or product‑led environment.
- Deep hands‑on knowledge of Databricks, Apache Spark, and Delta Lake, including large‑scale or near real‑time workloads.
- Strong proficiency in Python, SQL, and cloud data services (Azure preferred, but any major cloud is fine).
- Experience designing and operating end‑to‑end data and analytics architectures.
- Good understanding of BI tooling (e.g. Power BI, Tableau) and analytics modelling.
- Strong grasp of ETL/ELT orchestration, data quality frameworks, and observability tooling.
- Familiarity with governance practices including lineage, cataloguing, and data integrity standards.
- Awareness of data security, access controls, and compliance considerations.
- Experience with CI/CD, infrastructure‑as‑code, and cost‑optimised cloud engineering.
- Confident communicator, comfortable working with both technical and non‑technical teams.
- Naturally curious and motivated by delivering new insights and data products using modern tooling.
Why This Role?
- Chance to lead and grow a talented team while remaining hands‑on technically.
- Ownership of a modern data platform with strong influence on architecture and future direction.
- Opportunity to deliver customer‑facing data products with real business impact.
- Collaborative environment with the freedom to innovate and use emerging technologies.
Seniority level: Mid‑Senior level
Employment type: Full‑time
Job function: Information Technology
Industries: Software Development
Lead Data Engineer employer: OpenSource
Contact Detail:
OpenSource Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Engineer
✨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 a data engineering role. Building relationships can lead to job opportunities that aren’t even advertised.
✨Show Off Your Skills
Don’t just talk about your experience; demonstrate it! Create a portfolio showcasing your projects, especially those involving Databricks, Spark, or any cool data pipelines you've built. This will give potential employers a taste of what you can do.
✨Ace the Interview
Prepare for technical interviews by brushing up on your Python and SQL skills. Be ready to discuss your approach to data quality and governance, as well as how you’ve led teams in the past. Confidence is key, so practice makes perfect!
✨Apply Through Our Website
We’ve got some fantastic roles waiting for you, so don’t hesitate to apply 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!
We think you need these skills to ace Lead Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences mentioned in the job description. Highlight your leadership experience, especially in data engineering, and any hands-on work with Databricks or Spark. We want to see how you can shape our data platform!
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 background aligns with our needs. Don’t forget to mention your experience with cloud services and BI tools — we love a good story!
Showcase Your Projects: If you've worked on relevant projects, make sure to include them! Whether it's building scalable pipelines or improving data quality, we want to see concrete examples of your work. This helps us understand your hands-on capabilities and technical influence.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at OpenSource
✨Know Your Tech Inside Out
Make sure you brush up on your knowledge of Databricks, Apache Spark, and Delta Lake. Be ready to discuss how you've used these technologies in past projects, especially in building scalable data pipelines and improving data quality.
✨Showcase Your Leadership Skills
Prepare examples that highlight your experience leading or mentoring teams. Think about specific situations where you guided a team through challenges or helped them grow, as this role is all about hands-on leadership.
✨Understand the Data Lifecycle
Be ready to talk about the end-to-end data lifecycle. Discuss your experience with ingestion, transformation, and analytics, and how you've ensured performance and reliability in your previous roles.
✨Communicate Clearly with All Stakeholders
Practice explaining complex technical concepts in simple terms. You'll need to work closely with both technical and non-technical teams, so being able to bridge that gap will be crucial during your interview.
