Analytics Engineer

Job Description

Job Description

TITLE

Analytics Engineer III

WHAT YOU WILL ACHIEVE

You will take ownership of how Addgene's data is structured, documented, and maintained, and establish the standards and practices that define how the organization works with data. Your work will build a data warehouse that earns trust by deploying engineering best practices where they are currently missing, validating the datasets that drive decisions, and enabling anyone on the team to understand how any result was produced.

POSITION REPORTS TO

Director of Data Science

APPROXIMATE START DATE

October 2026

SALARY 

$117,000 - $120,000 annually + our amazing benefits (see below)!

LOCATION & POLICY ON REMOTE WORK

This is a hybrid position that requires at least two days a week onsite at our Watertown, Massachusetts Headquarters. Our beautiful office space has free, ample parking with a complimentary shuttle from Harvard Square and is on multiple bus lines, with the cost of public transit covered by Addgene for those onsite at least twice a week.

The Role:

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

Skills and Competencies

• 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

Benefits

At Addgene, we prioritize our people with comprehensive benefits that support both your work and your life.

Full-time employees receive:

• 85% company-paid healthcare premiums including dental (extending to family members) and 100% company-paid deductible
• Life, short-term, and long-term disability insurance.
• 22 days of PTO on top of our winter break closure and company holidays. ‌
• A 403(b) retirement account with a company match of 4% after 2 years of employment. ‌
• Annual professional development stipend
• Annual lifestyle stipend
• Fully paid public transportation for employees who work onsite four or more days per week. ‌
• Onsite yoga classes & wellness reimbursement
• Pet insurance
• Annual financial planning one-on-ones with our plan administrator

The Organization:

Addgene is a self-sustaining, thriving nonprofit life sciences organization that was created to help scientists share DNA-based research materials called plasmids. ‌Our mission is to accelerate research and discovery by improving access to useful research materials and information. Since its founding in 2004, Addgene has grown and innovated in order to assist the research community by archiving and sharing useful DNA-based research materials and resources (such as plasmids, viral vectors, and recombinant antibodies) with scientists worldwide. Broad availability of these unique research materials promotes the advancement of research in a wide variety of disciplines, including cancer, heart disease, and neurodegenerative disorders, while increasing collaboration within the scientific community.