Senior Data Engineer

National Alliance on Mental Illness

36d•Remote

About The Position

What started as a small group of families gathered around a kitchen table in 1979 has blossomed into the nation's leading voice on mental health. The National Alliance on Mental Illness (NAMI) is the nation's largest grassroots mental health organization dedicated to building better lives for the millions of Americans affected by mental illness. Today, we are an alliance of hundreds of local affiliates, state organizations and a national office that work in communities across the United States to raise awareness and provide support and education that was not previously available to those in need. NAMI advocates for all who are affected by mental illness, both the individuals and the people in their lives. We work to address disparities and injustices and to promote dignity and inclusion for all people with mental illness and their families. In addition to being advocates, we educate, we listen, and we lead as evidenced by our public awareness campaigns, the range of programs we provide, and our strong public policy. We currently have an opening for a Senior Data Engineer. As NAMI's first dedicated Data Engineer, the individual in this role will architect and build our data pipeline from the ground up, balancing near-term reporting needs with a durable, scalable foundation for future growth. The Senior Data Engineer is part of the Software Engineering team responsible for building and operating data pipelines, curated datasets, and reporting foundations that enable NAMI to measure program impact, support affiliate reporting, and drive data-informed decisions. This role partners closely with Engineering, Salesforce, Programs & Services, Success Team, and the Data Team to improve data quality, reduce manual reporting workflows, and deliver trusted, well-documented data products. This full-time position is remote. Salary Range: $95k - $105k

Requirements

5+ years of experience in data engineering, analytics engineering, or backend engineering with significant data ownership
String proficiency in Python (preferred) or another programming language used for data engineering
Strong SQL skills and experience building production-grade data models for reporting and analytics
Experience using managed or self-hosted EL tools such as Airbyte or Fivetran to ingest and maintain sources
Experience building analytics platforms using a modern data warehouse (e.g., MotherDuck, BigQuery, Redshift, Databricks, Snowflake), and a modern data lake (e.g., Amazon S3, Azure Blob Storage)
Hands-on experience with transformation tools (dbt Core or equivalent)
Experience in modeling, testing, monitoring, and building CI/CD for data pipelines and transformations (including dbt tests and deployment practices)
Experience with orchestration/workflow tools (e.g., Airflow, Dagster, Prefect) and scheduling reliable jobs
Experience running transformation jobs in CI/CD tooling such as GitHub Actions (e.g., PR validation, scheduled runs, environment-based deployments)
Experience with data visualization tools such as Tableau, Looker, Power BI, or Metabase
Familiarity with access controls and least-privilege practices for datasets and reporting layers, including managing data access across teams and roles
Hands-on experience with Docker
Strong proficiency in Git/GitHub
Strong debugging skills and ability to troubleshoot data issues end-to-end (source → pipeline → model → dashboard)
Excellent communication and collaboration skills; comfort working cross-functionally with non-technical partners
Comfort in a remote-first workplace
Well organized team player
Must pass background check

Responsibilities

Design, build, and maintain scalable ELT/ETL pipelines integrating data from operational systems, program reporting tools, forms, and third-party services
Build and maintain canonical datasets and data models that support consistent reporting across program types and implementations
Implement data quality checks, validation rules, and automated monitoring/alerting for critical data products
Develop and maintain transformations using analytics engineering best practices (e.g., modular, testable, well-documented SQL/Python), including models, tests, and documentation
Configure and support automated job runs via a CI/CD pipeline (e.g. Github Actions) to enforce testing and promote reliable releases
Partner with stakeholders to translate reporting needs into clear data requirements and maintainable implementations
Improve data accessibility through role-based access controls and curated data marts for dashboards and self-service analytics
Create and maintain documentation (data definitions, lineage, runbooks) for both technical and non-technical audiences
Optimize pipeline reliability, performance, and cost across storage, compute, and orchestration (including optimizing workloads, file layouts, and incremental processing patterns)
Orchestrate and monitor workflows, including retries, alerting, and runbooks
Work with tooling for observability, uptime, and performance monitoring
Be a collaborative colleague who learns from and educates their team
Proactively communicates, maintains expectations, and keeps work records up to date in project management systems and team communication channels
Stay current with industry trends and identify new ways for the team to improve
Other duties as assigned

Benefits

Generous and comprehensive Health, Dental, and Vision Plans
Paid Time Off: Vacation, Personal, and Sick Leave
Paid Parental Leave
403(b) retirement plan
Flexible Spending Accounts for health care, dependent care and commuter expenses
Life Insurance and Disability coverage paid by NAMI
Flexible Work and Telework programs
Professional Development Reimbursement program
A variety of wellness offerings to support team members
Employee Referral Program
The Employee Assistance Program (EAP) which provides support for personal and family problems common in contemporary life