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# Analytics Engineer

- **Company:** Addgene, Inc.
- **Location:** Watertown, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $117,000.0 - $120,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, BigQuery, Code Review, Data Validation, Information Engineering, Data Transformation, Data Security, Data Sharing, Data Warehousing, Database Queries, Python (Programming Language), SQL Databases, Scripting, Information Technology, Software Version Control, Data Pipelines
- **Published:** September 6, 2026
- **Apply:** https://www.careerjet.com/jobad/us8d7c1566b48ee236ea3042161c09731c

## About the Role

* 5+ years of experience in an analytics engineering role
 * A Master's degree in Computer Science or a related field is preferred; a Bachelor's degree with equivalent professional experience is also acceptable
 * Hands-on experience owning and managing a data warehouse, such as BigQuery or comparable platform, with track record of not just maintaining what exists, but making decisions about how it should be structured and rationale behind it
 * Experience with dbt for data transformation and warehouse management is highly preferred
 * Strong proficiency with SQL, including complex queries, joins and aggregations
 * Proficient with Python for data engineering tasks (pipeline development, scripting, automation)
 * Experience with data validation, quality assurance, and documentation practices
 * Familiarity with AI tools or agents in data engineering context
 * Interest in the life sciences and a willingness to develop working knowledge of the science behind the data related to your work
 * Demonstrated ability to identify problems proactively, and follow through without close supervision
 * Experience in a small, cross-functional team where priorities shift and individual judgment matters

## Description

Our data team functions as an internal core facility, serving partners across scientific, commercial, and business development functions. We are looking for an Analytics Engineer who will take ownership of the infrastructure that powers our data, bringing engineering discipline, sound judgment and responsibility for the systems they build and maintain.
This is a role for someone who finds satisfaction in making messy things reliable. You will inherit a complex data environment, and your job will be to understand it thoroughly, and implement deliberate improvements. The work is technical at its core, but requires deep analytical thinking that catches problems before they surface and ability to push tasks forward to completion.
To succeed in this role, your work will span three areas:
Data warehouse ownership

 * Take end-to-end ownership of our BigQuery data warehouse, including organization, documentation, and maintenance
 * Apply software engineering best practices for data transformation and modeling, bringing consistency and version control to how tables and views are built and updated
 * Assess internal data sources not yet represented in the warehouse and design pipelines to ingest, validate, and integrate them
 * Build and support reliable datasets as the foundation for dashboards and reporting
 * Manage tasks, assess priority and scope, and set realistic expectations

Data quality and validation

 * Create and maintain data quality checks that catch problems before they reach downstream users
 * Develop and operationalize a shared data lexicon to establish consistent definitions that can be applied across tables, dashboards, and reports
 * Expand instructions for BigQuery tables and views to improve the reliability and usefulness of AI-assisted data access
 * Write reliable, readable Python for wrangling, transformation, and reporting tasks

Pipeline development and maintenance

 * Build and maintain robust data pipelines that bring critical internal data sources into the warehouse reliably and on schedule
 * Improve the coverage and resilience of existing pipelines, reducing fragility and increasing the value of our data
 * Contribute to code review, applying and upholding engineering standards across the team's codebase
 * Identify opportunities to automate manual or repetitive processes, freeing team capacity for higher-value work


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