Principal Data Engineer

DriveWealth•Austin, TX

1d•$220,000 - $240,000•Hybrid

About The Position

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.

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.
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.
Applicants must possess the legal right to work in the country where the position is located at the time of application.
For US-based roles: Applicants must be currently authorized to work in the United States on a full-time basis without the need for current or future visa sponsorship.

Nice To Haves

Experience implementing Airflow or similar orchestrators.
Experience with Sigma Computing (from a data modeling perspective).
Experience building Data Apps within Databricks.
Experience using AI/LLM tools to enable faster, smarter analytics workflows.

Responsibilities

Own the full lifecycle of data products, from initial conceptualization and architecture through to production deployment, optimization, and maintenance.
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).
Write production-grade Python scripts for advanced data processing, anomaly detection, and custom orchestration logic that SQL cannot handle alone.
Take ownership of the "hardest problems" regarding query performance. Refactor legacy code and optimize incremental loading strategies to reduce costs and latency at scale.
Own the technical implementation of our data deployment reporting pipelines (Git, dbt Cloud), ensuring robust version control and seamless integration.
Engineer automated testing frameworks and validation suites (using dbt tests/Python) to ensure data integrity for critical business layers.
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.
Partner directly with stakeholders across Product, Finance, Operations, Risk, and Trading to translate domain-specific requirements into robust data products and reporting solutions.
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.