Principal Data Analytics Engineer

Join our team as a Principal Data Engineer, where you will be responsible for building innovative data products that provide actionable insights and empower our internal teams and partners. You will curate and maintain key data sources, spend 60-70% of your time writing code, and shape the architectural vision of our data ecosystem. Your role will involve end-to-end ownership of data products, core development, performance engineering, technical architecture, and cross-functional impact. You will also have the opportunity to mentor and guide other engineers.

• Conception, architecture, and implementation of data products, ensuring high-quality delivery.
• Ownership of the full lifecycle of data products, from initial conceptualization to production deployment.
• Collaboration with cross-functional teams to translate domain-specific requirements into robust data products.

• Ability to independently diagnose and resolve complex errors or issues within distributed systems (Spark/Databricks)
• 8+ years of professional experience in analytics engineering or data engineering, with a proven track record of building and scaling analytical data ecosystems
• Experience treating data as software by implementing unit testing, CI/CD, comprehensive documentation, and SLA monitoring
• Experience with dbt materializations, macros, and package management
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field
• Advanced proficiency in Python for data manipulation (Pandas/Polars/Spark) and interaction with APIs/AWS services
• Experience building Data Apps within Databricks
• Experience using Databricks for analytics workloads, including building and optimizing data models using Databricks SQL and dbt
• Experience with Sigma Computing (from a data modeling perspective)
• Experience implementing Airflow or similar orchestrators
• Experience architecting data models for FinTech and Capital Markets, including trade lifecycles, clearing/settlement, risk models, and financial reporting
• Proven ability to build data solutions that are reusable and modular, rather than one-off scripts
• Expert proficiency in SQL, with experience optimizing complex queries and data models at scale
• 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
• Experience using AI/LLM tools to enable faster, smarter analytics workflows