At a Glance
- Tasks: Lead the design and optimisation of scalable data architectures using Azure Databricks and PySpark.
- Company: Join a forward-thinking tech company in London with a hybrid work culture.
- Benefits: Enjoy competitive salary, private medical insurance, and perks like free lunches and on-site massages.
- Other info: Great opportunities for learning, development, and career growth.
- Why this job: Shape large-scale data ecosystems and drive innovation in AI-driven solutions.
- Qualifications: Experience in Azure Databricks, PySpark, and strong leadership skills required.
The predicted salary is between 63000 - 77000 £ per year.
We're looking for a Senior Data Engineer – Tech Lead (Databricks, PySpark) to join our team in London, UK, in a hybrid working mode. In this role, you will lead the design, development and optimization of scalable cloud-native data architectures, focusing on Azure Databricks, PySpark and Lakehouse principles. You will work hands-on to deliver performant data solutions for high-volume workloads, ensuring governance, reliability and best practices for enterprise-grade platforms.
As a technical leader, you will define data strategies, drive modernization initiatives and mentor engineers, fostering excellence and innovation throughout the team. This position offers the opportunity to shape large-scale data ecosystems, implement modern engineering practices and enable next-generation analytics and AI-driven solutions.
Responsibilities- Lead the architecture, design and build of large-scale data platforms using Azure Databricks and modern cloud technologies.
- Implement and optimize ETL workflows and streaming pipelines with PySpark and Delta Live Tables following Lakehouse principles.
- Enhance performance, manage cloud costs and ensure platform reliability for structured streaming workloads.
- Define data governance, security and quality standards to maintain consistency across the platform.
- Collaborate with stakeholders to translate complex business requirements into actionable technical solutions.
- Develop integration approaches using Azure-native services such as Data Factory, Synapse and Blob Storage.
- Mentor data engineers, promote modern engineering practices and perform technical reviews.
- Drive adoption of CI/CD, Infrastructure as Code and automated testing in data engineering environments.
- Implement observability and monitoring using tools like Databricks Workflows and related frameworks.
- Contribute to AI-driven initiatives by leveraging Databricks ML/MosaicML to integrate Generative AI and LLM-based solutions.
- Bachelor’s or Master’s degree in Computer Science, Software Engineering or related field.
- Extensive experience designing and implementing production-grade platforms using Azure Databricks.
- Expertise in PySpark, including advanced optimization, data skew mitigation and query tuning.
- Strong programming skills in Python with knowledge of modern software design principles.
- Practical experience with structured streaming, Delta Lake and Delta Live Tables.
- Proven experience in Lakehouse migration and modernization using open table formats such as Delta Lake or Apache Iceberg.
- Proficiency with cloud-native services on Azure and knowledge of multi-cloud environments (AWS or GCP).
- Hands-on experience with CI/CD and Infrastructure as Code tools (Terraform, GitHub Actions, Jenkins).
- Strong leadership ability to guide teams, define epics/user stories and ensure delivery in agile environments.
- Excellent communication and stakeholder management skills for both technical and non-technical audiences.
- Experience operationalizing LLM or Generative AI workflows in Databricks pipelines.
- Familiarity with frameworks like LangChain, LlamaIndex or Databricks ML/MosaicML.
- Knowledge of AI governance, security practices and enterprise integration controls.
- Background in financial trading data or related domains.
- Official Databricks certifications such as Certified Data Engineer Professional or Apache Spark Developer.
- EPAM Employee Stock Purchase Plan (ESPP).
- Protection benefits including life assurance, income protection and critical illness cover.
- Private medical insurance and dental care.
- Employee Assistance Program.
- Competitive group pension plan.
- Cyclescheme, Techscheme and season ticket loans.
- Various perks such as free Wednesday lunch in-office, on-site massages and regular social events.
- Learning and development opportunities including in-house training and coaching, professional certifications, and courses.
- If otherwise eligible, participation in the discretionary annual bonus program.
- If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program.
Data Engineer - Tech Lead (Databricks, Pyspark) employer: EPAM Systems, Inc.
EPAM Systems, Inc. is an exceptional employer that fosters a dynamic and inclusive work culture, offering employees the flexibility of a hybrid working model in the vibrant city of London. With a strong focus on professional development, team members are encouraged to grow their skills and advance their careers while contributing to impactful digital transformation projects in the energy sector. Joining EPAM means being part of a forward-thinking company that values innovation and collaboration, making it an ideal place for those seeking meaningful and rewarding employment.
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We think you need these skills to ace Data Engineer - Tech Lead (Databricks, Pyspark)
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
