Product Analytics Data Builder, Senior Associate

athenahealth•Boston, MA

11d•$86,000 - $146,000•Remote

About The Position

Build and enhance data models that power product, operations, strategy, and customer-facing analytics across athenahealth. In this role, you will translate business requirements into standardized, high-quality data assets within the Analytics Data Warehouse (ADW), with a focus on reliability, consistency, and usability. You will develop star schema models, fact and dimension tables, and supporting ETL processes that help teams access trusted data. This role is remote/hybrid/in-office based on team needs and reports to the Product Analytics Senior Manager.

Requirements

Bachelor’s degree required; degree in Computer Science, Data Engineering, Data Science, Statistics, Analytics, Information Systems, or a related quantitative field preferred.
3+ years of overall professional experience, preferably including 2–3 years in data engineering or data warehouse development.
Strong SQL skills, including experience building and optimizing data models in a cloud data warehouse environment; Snowflake experience preferred.
Experience with ETL tools and processes; Airflow preferred.
Experience with Python, including use of Jupyter Notebooks for data analysis, exploration, profiling, and reusable solutions.
Familiarity with data visualization tools such as Sigma, Tableau, or Power BI, and understanding of how data models support effective visualization.
Understanding of dimensional modeling concepts and star schema design patterns.
Ability to translate complex business requirements into well-structured data models.
Experience performing data validation checks to support data quality and integrity across pipeline stages.
Strong written and verbal communication skills with the ability to work effectively across teams.

Responsibilities

Translate business requirements from Product Managers and data consumers into effective star schema data models.
Develop fact and dimension tables in the Analytics Data Warehouse (ADW) using established design patterns and standards.
Implement ETL processes that support data freshness, reliability, and performance.
Document data models, lineage, and business definitions to support broader use of the data.
Conduct data exploration and profiling to understand source data during discovery, testing, validation, and documentation.
Build Python-based solutions, such as Jupyter Notebook workflows, to automate repetitive tasks and improve validation and analysis processes.
Use AI-enabled tools that are available to streamline data exploration, validation, documentation, or coding tasks while applying sound judgment to confirm results and maintain data quality.
Reinforce the use of standardized data assets and metrics, and help identify and address gaps as they are discovered.
Support internal and external stakeholder needs by ensuring data models meet analytical requirements.
Develop and maintain collaborative relationships with product teams and other business partners.
Communicate technical concepts clearly to technical and non-technical audiences.
Provide rationale for design decisions to support alignment and adoption.
Contribute to improvements in data development practices, patterns, and reusable tooling.
Assist with issue investigation and troubleshooting across data pipelines and data assets.