PadSplit is the nation's largest coliving marketplace, connecting renters seeking affordable housing with thousands of furnished rooms in shared residential properties across the U.S. The platform transforms how people access housing by eliminating traditional rental barriers—no long-term leases, minimum credit scores, or large upfront costs required.

Members enjoy all-inclusive weekly payments that cover rent, utilities, Wi-Fi, and furniture, allowing for move-ins within 48 hours. This flexible approach makes housing accessible to essential workers, students, and anyone navigating today's challenging rental market.

For property owners and investors, PadSplit maximizes rental income by converting underutilized spaces into private rooms within managed coliving environments. The platform provides a turnkey solution that generates higher returns while addressing the housing affordability crisis—creating value for both sides of the marketplace.

PadSplit

The nation’s largest coliving marketplace.

The Role We Need
PadSplit is growing its analytics platform and needs a hands-on 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 For
We'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.
 
Notice to Applicants:
 
PadSplit participates in E-Verify. All new employees are required to complete an I-9 form and be authorized to work in the United States. Employment is contingent upon successful completion of the E-Verify process.
 
PadSplit is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Compensation is based on the role's scope, national market benchmarks, the person's expertise and experience, and the impact of their contributions to our business goals.

Data Engineer (Fully Remote)

What the job involves