Data Engineer I

Inroads•Washington, DC

2h•Remote

About The Position

We’re hiring an entry-level Data Engineer to join our Data Engineering team. You’ll take ownership of building reliable ELT/ETL pipelines and strong analytical data models using dbt. You don’t need to know everything on day one, but you must be eager to learn, experiment, and grow quickly.

Requirements

2 years of experience in data engineering, analytics engineering, or related field (internships, coursework, and projects encouraged) OR an undergraduate degree or higher in a related field
Proficiency in SQL; able to write clean, performant queries.
Practical experience with dbt (models, tests, documentation, sources; familiarity with Jinja/macros is a plus).
Solid grasp of Kimball dimensional modeling (star/snowflake schemas, facts and dimensions, SCDs).
Demonstrated ability to translate complex business logic into efficient, technical data structures.
Understanding of data warehousing and ELT/ETL patterns.
Familiarity with Git and collaborative development workflows.
Strong problem-solving and communication skills; attention to detail.
Demonstrated willingness to learn quickly and take ownership of outcomes.

Nice To Haves

Workflow orchestration experience (Airflow, Dagster, Prefect).
Cloud data warehouse exposure (Snowflake preferred).
Basic Python skills for data tasks (ETL scripts, API integrations).
Experience with data testing/observability tools and lineage documentation.
Experience designing and implementing data models specifically for consumption by internal technical teams (Data Analysts, Data Scientists).
Exposure to CI/CD for analytics (dbt Cloud/Jobs, GitHub Actions).
Domain knowledge of the U.S. healthcare insurance marketplace (Healthcare.gov) data and metrics.

Responsibilities

Build, test, and document dbt models (staging, intermediate, marts) aligned to Kimball dimensional modeling best practices.
Implement data quality checks (dbt tests, sources, freshness) and contribute to monitoring data reliability.
Collaborate with analytics, product, and domain stakeholders to translate business needs into well-modeled datasets.
Proactively audit, define, and standardize key business metrics in collaboration with stakeholders to ensure consistency and trust across reporting platforms.
Support source onboarding and ingestion from APIs, files, or databases into the warehouse.
Maintain clear documentation for datasets, lineage, and transformation logic.
Assist with CI/CD for analytics code (Git workflows, PR reviews, environments).
Optimize model performance (incremental strategies, partitions/clustering, query tuning).
Troubleshoot pipeline/data issues and contribute to root-cause analyses.
Proactively identify improvement opportunities and drive them to completion with high ownership.