About the Company
PadSplit is a housing-focused company working to address the affordable housing crisis. Its work combines technology and business innovation to expand access to housing. The company offers employees the opportunity to contribute to its mission while working in a collaborative, remote environment.
About the Role
PadSplit is seeking a hands-on Data Engineer to help expand and maintain its analytics platform while working alongside the existing Data Engineering Lead. The role focuses on building reliable data ingestion and transformation pipelines using Dagster or Airflow, dbt, Snowflake, Python, Airbyte, and AWS.
The successful candidate will develop pipelines and data models, support critical production workflows, troubleshoot failures, and maintain consistent data quality across full refreshes and incremental loads. The role emphasizes collaborative development, reviewed pull requests, reliable pipeline operations, and close partnership with analytics and product teams.
Key Responsibilities
Develop and maintain Dagster jobs, assets, schedules, and sensors, along with dbt models, tests, and documentation.
Build and support data ingestion and transformation pipelines across Dagster or Airflow, dbt, and Snowflake.
Maintain critical daily data workflows, particularly the PostgreSQL-to-Snowflake-to-dbt pipeline and third-party API integrations.
Write and debug Python pipelines for REST API synchronization, large PostgreSQL extracts, Parquet data loads, and Snowflake COPY operations.
Configure and troubleshoot Airbyte connections where managed data synchronization is appropriate.
Monitor production pipeline runs and investigate failures involving AWS IAM permissions, memory constraints, Spot instances, and incorrect data watermarks.
Execute backfills and incremental catch-ups while documenting what data was processed and why.
Collaborate with analytics and product teams on dimensional modeling, including dimension and fact tables, incremental strategies, and data quality.
Apply appropriate full-refresh or incremental-loading strategies while maintaining idempotency and supporting reliable backfills.
Submit and review pull requests with clear, scoped changes, appropriate tests, and attention to potential failure scenarios.
Make occasional Terraform updates for secrets, environment variables, and job sizing when required.
Support release preparation, code reviews, and the creation of concise runbooks that help teammates operate pipelines.
Maintain reliable data models and healthy pipelines while ensuring operational knowledge is shared across the team.
Required Qualifications
Strong understanding of relational databases and data warehouse fundamentals, including keys, data grain, normalization, star schemas, and slowly changing dimensions (SCDs).
Hands-on experience with Dagster or Apache Airflow, including practical orchestration work beyond writing SQL in a scheduling interface.
Experience building and maintaining dbt models, tests, and documentation.
Strong Python skills, including working with data at scale across extraction, transformation, and loading processes.
Practical working knowledge of AWS services, particularly S3, IAM, and ECS/Fargate, with the ability to troubleshoot pipeline execution issues.
Experience using Airbyte or similar extract-and-load tools.
Ability to develop clear, reviewable pull requests and provide thorough code reviews.
A reliability-focused approach to pipeline maintenance, data consistency, incremental processing, and production support.
Location and Work Arrangement
This is a fully remote position open to candidates authorized to work in the United States. The listing references Kentucky and notes that the Atlanta, Georgia location displayed on some job platforms is an administrative requirement rather than an indication that the role is office-based.
Salary and Benefits The annual salary range is $150,000 to $165,000. Compensation depends on the scope of the role, national market benchmarks, the candidate’s expertise and experience, and expected business impact. Additional variable compensation is performance-based.
Benefits include
Equity incentive plan and company-wide bonus opportunity.
National medical, dental, and vision insurance plans.
Company-provided life insurance.
Optional accident insurance, flexible spending account (FSA), and dependent care flexible spending account (DCFSA) benefits.
Unlimited paid time off (PTO) and 11 company-observed holidays.
401(k) plan.
Twelve weeks of paid leave for birth and non-birth parents.
Interview Process
Applications are reviewed by a member of the PeopleOps team.
Eligible candidates may be invited to a 30-minute video interview with PeopleOps.
The next stage may be a 45-minute video interview with the Principal Data Scientist.
Candidates who progress may participate in a 90-minute video panel interview with key stakeholders.
The final interview, if required, is a 30-minute video meeting with a company leader.
Successful candidates may then receive an offer.
Employment Eligibility
PadSplit participates in E-Verify. New employees must complete Form I-9 and be authorized to work in the United States. Employment is contingent on successfully completing the E-Verify process.
Equal Employment Opportunity
PadSplit is an equal opportunity employer. All qualified applicants receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.