Principal Data Engineer

Drive Wealth is on a mission to make investing easier. We believe that everyone should have the ability to control their financial future, and that access to financial markets should not be limited by geography, wealth, or legacy systems. We are a global B2B financial technology organization dedicated to democratizing access to financial independence around the world. Our mission is realized through an API-based platform, empowering our partners to offer seamless investing and trading experiences to clients worldwide, all from their mobile devices.

Our technology provides partners with a modern, extensible toolkit, enabling traditional investment workflows and innovative techniques like fractional share ownership. Drive Wealth has evolved into a global platform offering trading of US equities, mutual funds, ETFs, fixed income, and options.

There’s never been a better time to build a category-defining business and there has rarely been a team better positioned for this opportunity. Our culture blends the pace and agility of a fintech start-up with the impact, stability, and discipline of Wall Street. We encourage creativity and experimentation while ensuring institutional-grade execution and regulatory compliance in everything we do. Join us and help build the future of global investing!

We are looking for a Principal Data Engineer who thrives on solving the most complex data problems, and enjoys working cross-functionally with product, engineering and analytics teams to deliver trusted, high-impact data solutions. The ideal candidate is excited to take strategic ownership of complex data challenges and help evolve our data platform to the next stage of scale and maturity, driving architectural vision and execution.

The Data & Analytics organization at Drive Wealth powers the company’s data ecosystem end-to-end. Within it, the Data Platform Engineering team owns two critical pillars:

• Data Ingestion – building reliable pipelines that bring data from diverse internal and external sources
• Semantic Data Layer – creating curated, consistent and reliable datasets that enable analytics, reporting and decision making

• Define the architectural vision and strategy for our data ecosystem, ensuring scalability, reliability, and security.
• Design, build and maintain mission‑critical robust data pipelines (batch, streaming and replication) that power analytics, reporting and compliance use‑cases.
• Translate business needs into comprehensive data solutions, acting as a technical leader partnering with product managers, engineering teams and business stakeholders.
• Proactively identify major architectural challenges and opportunities, anticipate scaling, performance and quality needs before they become blockers.
• Establish and champion company‑wide best practices, including documentation, code reviews and standards for data modelling, orchestration and tooling.
• Mentor and coach other engineers, raising the bar for data engineering craft and helping grow team and organizational capabilities.
• Contribute to self‑service data products, enabling data consumers with curated datasets, semantic layers and tools that accelerate insight generation.
• Drive the full lifecycle of data platform modernization initiatives, from ideation to successful rollout.

• 7+ years of professional experience in data engineering or related fields, with a proven track record of building and scaling data platforms and ecosystems from 0 to 1 and leading modernization efforts.
• Technical
  
  Skills:
• Proven ability to drive technical strategy, work independently, and lead complex projects cross‑functionally with product, engineering and business teams.
• Experience working in teams with CI/CD pipelines, Git‑based workflows and Agile methodologies.
• Curiosity, accountability and a drive to improve data quality, scalability and usability across the company.
• BS in Computer Science or equivalent.

• Familiarity with regulatory and compliance‑driven reporting (e.g. FINRA, GDPR)
• Previous experience as a Software Engineer (SWE)
• Exposure to and understanding of ML and AI data pipelines and MLOps
• E…