Senior Data Engineer (Distributed Data Processing)

• Develop production-grade Python data workflows that implement complex business logic.
• Work with Databricks for job execution, orchestration, and optimisation.
• Own and optimise cloud-based data infrastructure (AWS preferred, Azure also relevant).
• Optimise data workloads for performance, reliability, and cost.
• Collaborate with engineers, domain specialists, and delivery teams on client-facing projects.
• Take ownership of technical initiatives and lead by example within the team.
• Support and mentor other engineers.

We’re looking for a Senior Data Engineer to join a data-intensive SaaS platform operating in a complex, regulated industry. This is a hands-on senior IC role focused on distributed data processing, Spark-based pipelines, and Python-heavy engineering. You’ll be working on large-scale batch data workflows that power pricing, forecasting, and operational decision-making systems. The role requires strong engineering judgement, the ability to operate autonomously, and the confidence to mentor others while delivering under tight timelines. This is not an ML, Data Science, or GenAI role., * Proven experience as a Senior Data Engineer.

• Strong Python software engineering foundation.
• Hands-on Spark experience in production (PySpark essential).
• Real-world experience using Databricks for data pipelines (Spark depth matters most).
• Experience with large-scale or parallel data processing.
• Ownership of cloud infrastructure (AWS and/or Azure).
• Comfortable operating with senior-level autonomy and responsibility.
• Experience mentoring or supporting other engineers.

Nice-to-Have Experience

• Experience working with time-series data.
• Background in utilities, energy, or other data-heavy regulated industries.
• Exposure to streaming technologies (Kafka, event-driven systems), though the role is primarily batch-focused.