Data Engineer - Tech Lead (Databricks, Pyspark)

We're looking for a Senior Data Engineer – Tech Lead (Databricks, Py. Spark) 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, Py. Spark 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 Py. Spark 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 Requirements 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 Py.

Spark, 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 Nice to have Experience operationalizing LLM or Generative AI workflows in Databricks pipelines Familiarity with frameworks like Lang.

Chain, Llama. Index 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