Principal Data Engineer, DriveWealth

• 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, comprehensive documentation, and SLA monitoring
• Ability to independently diagnose and resolve complex errors or issues within distributed systems (Spark/Databricks)
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field

Desirable

• As a Principal Data Engineer, Analytics 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
• 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