Senior Data Engineer

Project description

We are seeking a hands-on Data Engineer to support the Total Fund Management Portfolio Management Technology team. This role focuses on implementing reliable data solutions in a cloud-native environment using established platform tooling and patterns. You are expected to independently design and deliver data solutions with minimal oversight.

This is not a role for someone who requires detailed task breakdowns or constant direction. Once objectives and constraints are clear, you are expected to independently drive work to completion, proactively manage risks, communicate progress and trade-offs effectively, and manage stakeholder expectations.

Responsibilities

• · Own timelines and deliverables, proactively managing scope, dependencies, and execution risks · Independently frame, design, and deliver software solutions by engaging stakeholders, clarifying ambiguous requirements, and challenging assumptions · Build backend services and RESTful APIs aligned to those designs · Develop cloud-native applications (AWS preferred) · Implement workflow automation and data pipelines where needed · Contribute across the full software development lifecycle, including CI/CD · Ensure solutions are production-ready, including testing, documentation, and handover · Act as a reliable execution partner, proactively communicating progress, trade-offs, risks, and changes in expectations

SKILLS

Must have

• · Strong Python with PySpark · Hands-on experience with Databricks, including developing and operating data workloads · Schema design and data modelling experience · Working knowledge of Spark sufficient to build and troubleshoot PySpark workloads; deep Spark specialization is not required · Prior consulting, advisory, or client-facing delivery experience · Proven ownership and independent delivery without close supervision · Strong problem-solving skills, including the ability to frame and solve problems in ambiguous situations · Strong communication and stakeholder management skills, including proactive expectation management · Demonstrated adoption of AI-assisted engineering tools and practices as part of day-to-day engineering delivery

Nice to have

· Experience with Apache Iceberg · AWS data services (Glue, Lake Formation) · Workflow orchestration tools (Airflow) · Experience with distributed data processing architectures · Capital markets experience