Databricks Engineer – Project Delivery Manager

This role is for a Databricks Engineer with Project Delivery Manager responsibilities, focusing on leading Finance data domain platform and solution architecture, providing hands-on data engineering with Python, PySpark, and Databricks, and serving as a team lead for delivery execution. The role involves defining target-state designs, building scalable pipelines, and ensuring reliability and observability.

What you'd actually do

  1. Lead Finance data domain platform and solution architecture, defining target-state designs, integration patterns, and data standards aligned to enterprise strategy.
  2. Provide hands-on core data engineering leadership (Python, PySpark, Databricks) to build and evolve scalable pipelines, curated datasets, and reusable frameworks.
  3. Serve as team lead for delivery execution—prioritizing work, guiding design/code reviews, ensuring reliability/observability, and partnering with product and cloud/platform teams.
  4. Communicate regularly with Engagement Managers (Directors), project team members, and representatives from various functional and / or technical teams, including escalating any matters that require additional attention and consideration from engagement management
  5. Independently and collaboratively lead client engagement workstreams focused on improvement, optimization, and transformation of processes including implementing leading practice workflows, addressing deficits in quality, and driving operational outcomes

Skills

Required

  • Python
  • PySpark
  • Databricks
  • Data Architecture
  • Platform Architecture
  • Cloud-based data ecosystems
  • Data Engineering
  • Finance data domain
  • Data management fundamentals
  • Data modeling
  • Security/access controls
  • Data quality
  • Lineage
  • Documentation

Nice to have

  • Generative AI
  • Advanced analytics

What the JD emphasized

  • 8+yrs experience as a Data Architect / Platform Architect in cloud-based data ecosystems, designing end-to-end architectures (ingest, transform, storage, consumption, governance).
  • Strong hands-on expertise in Python, PySpark, and Databricks (jobs/workflows, performance tuning, Delta/medallion-style patterns, operational monitoring).
  • Demonstrated leadership of data engineering teams (technical direction, mentoring, delivery governance, stakeholder management), preferably within a Finance data domain.