Principal Data Engineer

About the role

DriveWealth 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. DriveWealth 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! As a Principal Data Engineer, you will be dedicated to building innovative data products that provide actionable insights and empower both our internal teams and partners to succeed. Your core focus will be on curating and maintaining key data sources and statistics that serve both internal and external stakeholders. You will act as the hands-on technical engine driving this work forward, spending 60-70% of your time writing code while shaping the architectural vision of our data ecosystem. You will architect for massive scale, treat data as software, and build highly performant, resilient models and reporting solutions (using Databricks, dbt, and Python) that fuel data-driven decision-making across the business.

Responsibilities

• Advanced Engineering & Coding
• End-to-End Ownership: Own the full lifecycle of data products, from initial conceptualization and architecture through to production deployment, optimization, and maintenance.
• Core Development: Design and code complex dbt models and data transformation logic for high-volume financial datasets (e.g., trade transactions, stock ledgers, and clearing/settlement records).
• Python Automation: Write production-grade Python scripts for advanced data processing, anomaly detection, and custom orchestration logic that SQL cannot handle alone.
• Performance Engineering: Take ownership of the "hardest problems" regarding query performance. Refactor legacy code and optimize incremental loading strategies to reduce costs and latency at scale.
• Technical Architecture & Standards
• CI/CD & DevOps: Own the technical implementation of our data deployment reporting pipelines (Git, dbt Cloud), ensuring robust version control and seamless integration.
• Data Quality as Code: Engineer automated testing frameworks and validation suites (using dbt tests/Python) to ensure data integrity for critical business layers.
• Leadership & Cross-Functional Impact
• Technical Project Leadership: Act as the hands-on lead for major initiatives. Scope, design, and manage complex data projects while remaining active in the codebase to ensure high-quality delivery.
• Cross-Functional Partnership: Partner directly with stakeholders across Product, Finance, Operations, Risk, and Trading to translate domain-specific requirements into robust data products and reporting solutions.
• Mentorship & Guidance: Act as the "go-to" technical resource for the team. Conduct thorough code reviews and help senior and junior engineers solve blockers through pair programming and architectural guidance.

Requirements

• 8+ years of professional experience in analytics engineering or data engineering, with a proven track record of building and scaling analytical data ecosystems.
• Expert proficiency in SQL, with experience optimizing complex queries and data models at scale.
• Advanced proficiency in Python for data manipulation (Pandas/Polars/Spark) and interaction with APIs/AWS services.
• Experience using Databricks for analytics workloads, including building and optimizing data models using Databricks SQL and dbt.
• Experience with dbt materializations, macros, and package management.
• Experience architecting data models for FinTech and Capital Markets, including trade lifecycles, clearing/settlement, risk models, and financial reporting.
• AI-Assisted Engineering: Proficiency in leveraging AI tools (e.g., GitHub Copilot,or similar LLMs) to accelerate code delivery, automate documentation, and optimize engineering workflows.
• Proven ability to build data solutions that are reusable and modular, rather than one-off scripts.
• Experience treating data as software by implementing unit testing, CI/CD, comprehensi

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