Own data pipelines and infrastructure: Design, implement, and evolve scalable, production-grade systems using tools such as Dagster, Airflow, Snowflake, AWS, MongoDB, and dbt. Apply a cloud-native and DevOps mindset using CI/CD, infrastructure-as-code, monitoring, and automated testing to build reliable systems. Partner with cross-functional teams to deliver solutions that meet both immediate product needs and long-term organizational strategy.
Lead data ingestion and integration: Bring in complex, high-volume datasets while ensuring strong data contracts, freshness, quality, integrity, and lineage, and build systems that empower domain experts to contribute to and maintain their own data pipelines.
Transform raw data into trusted data products: Convert raw inputs into structured, usable datasets that empower our analytical and product teams. Collaborate closely with operational data managers to ensure data models and intuitive, reliable alignment with how data is consumed in practice.
Leverage AI: Make informed judgement calls about how AI can be a force-multiplier for both your own work and the team’s and how it can’t.
Elevate the team: Mentor engineers, actively shape technical direction through architectural reviews and roadmap planning, and build team culture through documentation and knowledge sharing.

Requirements

6+ years of relevant experience in data engineering or a related field;
Deep experience designing and operating data pipelines and orchestration frameworks (e.g., Dagster, Airflow);
Strong understanding of ELT/analytics engineering patterns (e.g., dbt, dimensional modeling, data contracts);
Hands-on experience with cloud data platforms (e.g., Snowflake) and cloud infrastructure (e.g., AWS);
Proficiency in Python, containerization (Docker), and modern deployment patterns;
Experience working in lean, cross-functional teams and operating in environments with evolving requirements;
Strong written and verbal communication skills with the ability to explain technical concepts to non-technical partners.
Nice-to-haves
Familiarity with Voter File Data;
Experience with or interest in political data;
Background in political tech, civic tech, advocacy, or mission-driven organizations;
Experience within an engineering team providing technical support to other data functions (e.g., Data Scientists, Data Managers, etc.);
Experience applying AI/ML techniques to voter or political data.

Benefits

Health, vision, and dental insurance with 100% of premiums covered for you and qualifying family members;
Retirement benefits with a 5% employer match;
A flexible unlimited PTO plan;
Generous paid parental leave;
Pre-tax commuter benefits;
A company laptop;
A flexible remote work environment;
A home office setup stipend for all new employees;
Monthly reimbursement for remote work expenses;
A yearly professional development fund;
Mental health and wellness benefits through Calm and Better Help;
Yearly in-person staff retreats;
A welcoming culture that celebrates diversity, equity, inclusion, and belonging.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringdata pipelinesorchestration frameworksELTanalytics engineeringcloud data platformscloud infrastructurePythoncontainerizationAI/ML techniques

Soft Skills

mentoringcommunicationcollaborationtechnical directionknowledge sharingproblem-solvingadaptabilityteam culture buildingdocumentationexplaining technical concepts