Lead Analytics Engineer

at  Facet

We are looking for a meticulous and methodical Lead Analytics Engineer to join our Data Science & Analytics team.
Facet is a fully remote FinTech company with a mission to empower people to live more enriched lives by delivering a new standard of financial advice that elevates expectations across consumers and the industry. As a Lead Analytics Engineer, you will enable better insights programs and machine learning features by bridging the gap between our business operations and platform, and our data team. These insights and features will allow us to improve our programs and build a world class member experience.

TOOL KNOWLEDGE

• Extensive experience leveraging Python and SQL to solve data problems.
• Familiarity with building and maintaining data models in cloud environments like BigQuery or Snowflake, and leveraging supporting cloud compute technology on GCP or AWS.
• Experience monitoring and managing large projects and budgets on GCP or AWS.
• Advanced knowledge using SQL template engines like dbt and Airflow for constructing end-to-end data transformation pipelines.
• Experience with distributed data systems, such as Apache Spark for handling large datasets.
• Familiarity with machine learning frameworks such as scikit-learn, xgboost, and similar.
• Experience collaborating with other Analysts, Data Scientists, and ML Engineers using version control.
• Experience identifying source of truth system measures from 1st and 3rd party systems to support downstream Data Science, Machine Learning, and Analytics requirements.

USE CASE KNOWLEDGE

• Deep understanding of Data Science, Machine Learning, and Analytics workflows and how to set up data infrastructure that supports them.
• Extensive experience building foundational historical data models.
• Experience parsing unstructured data and translating it into simplified tabular formats for Analytics use cases.
• Advanced customer data modeling experience, including behavioral measurement aggregation and warehouse-side identity resolution.
• Experience working with financial data, including transactions, account balances, and holdings data is a plus.
• Familiarity with subscription direct-to-consumer sales and marketing data, including web analytics and ecommerce tracking, subscription sales terminology.
• Experience with multi-touch attribution modeling.

• Development and maintenance of core data models, designed for efficient use across Analytics, Data Science, and Machine Learning use cases.
• Efficient orchestration of data models to maximize accuracy and availability, and minimize cost.
• Manage relationships with core technology partners across Data Science & Analytics, Software Engineering, Business Operations, Cybersecurity, and Platform Engineering to ensure our data warehouse is providing the most accurate reflection of our business sources of truth.
• Employ business acumen and analytical skills to identify suspicious system behaviors and results, and track down issues before they impact downstream reporting and model outputs. It’s critical that you learn and understand our business operations and systems to excel in this role.