Lead Analytics Engineer

Description

Core Responsibilities:

• 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.

Requirements

Traits

• Exemplary communication skills, especially when communicating with non-data stakeholders. Your ability to effectively communicate the needs of data functions with other technical leaders will be key to performing this role.
• Very strong troubleshooting and analytical skills.
• Strong customer data privacy knowledge and sensibility.
• Strong aptitude for technology and automation, driven to learn constantly and share knowledge with others.
• Resourceful, creative, and comfortable working in small teams with minimal supervision to solve complex business problems.

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.

Benefits

• $160,000-$190,000 base salary + bonus
• Equity
• Flexible PTO
• All the benefits: medical, dental, and vision insurance, 401(k) with employer match, short and long term disability coverage (paid by Facet), life insurance options and paid parental leave
• Certification reimbursement program
• Work from anywhere in the US