PadSplit is growing its analytics platform and needs a hands-on Sr. Data Engineer to work alongside our existing DE lead. This person will build and maintain ingestion and transformation pipelines across Dagster (or Airflow), dbt, and Snowflake, with supporting work in Python, Airbyte, and AWS. The role combines building new pipelines and data models with providing real coverage on critical paths — especially the daily Postgres → Snowflake → dbt flow and third-party API loads — all shipped through reviewed pull requests rather than one-off scripts.
The Person We Are Looking ForWe're looking for a practitioner who thinks natively in dimensions, facts, and slowly changing dimensions — someone who knows when to use a full refresh versus an incremental load and how that choice affects idempotency and backfills. This person writes clear, reviewable PRs and gives equally thoughtful reviews, with attention to scoped diffs, sensible tests, and failure modes. They're comfortable in complex Python data flows and have enough AWS literacy to reason about task roles, buckets, and cross-account access without needing to own all of platform engineering.
Here's What You'll Be Doing Day-to-Day:
- PR-driven shipping: Opening and merging pull requests for new or updated Dagster jobs, assets, schedules, and sensors, plus dbt models, tests, and documentation.
- Infrastructure tweaks: Writing occasional Terraform for secrets, environment variables, or job sizing when a pipeline needs it.
- Pipeline implementation: Building and debugging Python pipelines covering REST/API syncs, large Postgres extracts, Parquet loads, and Snowflake COPY operations.
- Airbyte management: Configuring or troubleshooting Airbyte connections wherever managed sync is the right fit.
- Production monitoring: Watching production runs and investigating failures related to IAM, OOM, Spot instances, or bad watermarks.
- Backfills & catch-ups: Running backfills and incremental catch-ups with a clear story for what landed and why.
- Modeling partnership: Working with analytics and product on dim/fct/x_fct design, incremental strategies, and data quality.
- Code review & runbooks: Participating in code review, release prep, and writing short runbooks so others can operate your pipelines when you're out.
Here's What You'll Need to Be Successful:
- Warehouse fundamentals: Solid grasp of relational databases and warehouse patterns — keys, grain, normalization vs. star schema, and how SCD behavior gets encoded.
- Orchestration experience: Practical, hands-on Dagster (or Airflow) experience — not just writing SQL inside a scheduler UI.
- dbt proficiency: Real experience building and maintaining models, tests, and documentation in dbt.
- Python at scale: Comfort reading and writing Python that moves data at scale across extract, transform, and load steps.
- AWS working knowledge: Practical familiarity with S3, IAM, and ECS/Fargate at a "debug my job" level.
- EL tool familiarity: Experience with Airbyte or similar extract-and-load tools.
- PR discipline: The discipline to write pull requests others can easily review, and to give equally rigorous reviews in return.
- Reliability mindset: A track record of keeping pipelines healthy and modeling consistent across full refresh and incremental paths, without becoming a single point of failure.
The Interview Process:
- Your application will be reviewed for possible next steps by a real human being from the PeopleOps team.
- If you meet eligibility requirements, the next step would be a video interview with a member of the PeopleOps team for about thirty (30) minutes.
- If warranted, the next step would be a video interview with our Principal Data Scientist for forty-five (45) minutes.
- If warranted, the next step would be a video panel interview with key stakeholders at PadSplit for one and a half (1.5) hours.
- If warranted, the next and final step would be a video interview with a key leader in the company for thirty (30) minutes.
- If warranted, we move to offer!
Compensation, Benefits, and Perks:
- Fully remote position - we swear!
- Competitive compensation package including an equity incentive plan and company-wide bonus opportunity
- National medical, dental, and vision healthcare plans
- Company provided life insurance policy
- Optional accidental insurances, FSA, and DCFSA benefits
- Unlimited paid-time (PTO) policy with eleven (11) company-observed holidays
- 401(k) plan
- Twelve (12) weeks of paid time off for both birth and non-birth parents
- The opportunity to do what you love at a company that is at the forefront of solving the affordable housing crisis
Please note: Although the job posting says it's in Atlanta, Georgia, this is a fully remote position. This is a result of our Applicant Tracking System requiring a location to post the role on LinkedIn.
Skills Required
- Hands-on experience with Dagster or Airflow orchestration
- Practical experience building and maintaining dbt models, tests, and documentation
- Strong Python experience processing data at scale across extract, transform, and load workflows
- Solid understanding of relational databases, warehouse patterns, keys, grain, normalization, star schemas, and slowly changing dimensions
- Practical AWS knowledge, including S3, IAM, and ECS or Fargate
- Experience with Airbyte or similar extract-and-load tools
- Experience managing full refreshes, incremental loads, idempotency, backfills, and data quality
- Ability to write reviewable pull requests and participate in rigorous code reviews
- Experience monitoring production pipelines and troubleshooting failures
- Experience writing Terraform for pipeline infrastructure configuration
What We Do
At PadSplit we’re disrupting the affordable housing industry by creating safe, attractive, and respectable co-living environments. Our mission is to solve our affordable housing crisis one room at a time while leveraging housing as a vehicle for financial independence. If you’re motivated by the conviction that working people should have a decent place to live, and want to do something about it, come join us!
