[All remote jobs](https://jobicy.com/jobs.md)[![PadSplit logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2021/06/da525286873130ed0b95212896ef55d4.jpg)](https://jobicy.com/company/padsplit.md)[PadSplit](https://jobicy.com/company/padsplit.md) # Sr. Data Engineer Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/padsplit.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md)SalaryUSD 180k–200k / yrDepartment[Data Science & Analytics](https://jobicy.com/categories/data-science.md)EmploymentFull TimeExperienceSeniorPublished30 Oct 2026Apply before29 Nov 2026Listing views28Application actions0Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. PadSplit is seeking a senior hands-on data engineer to build, operate, and improve production data pipelines for its analytics platform. The role centers on Dagster or Airflow, dbt, Snowflake, Python, Airbyte, and AWS, including Postgres-to-Snowflake ingestion and third-party API loads. The engineer will own reliable, pull-request-driven delivery, investigate production failures, perform backfills, and contribute occasional Terraform changes. Close collaboration with analytics and product teams is required to develop dimensional models, incremental strategies, tests, documentation, and operational runbooks. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 4/5RelaxedFast-paced ### Autonomy Level 4/5GuidedFull ownership ### Communication Load 4/5IndependentCollaborative AI insightThis is a senior production data-engineering role requiring deep practical ability across orchestration, warehouse modeling, cloud debugging, and reliability practices. The engineer must safely manage critical pipelines, backfills, and failure scenarios while maintaining reviewable, well-tested code. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$190,000US market range (per year)$155k–$205k0$225.50k AI insightThe employer discloses a base compensation range of $180,000–$200,000 USD per year, with a midpoint of $190,000. This is a credible annual range for a senior U.S.-remote data engineering role and is positioned above a broad estimated U.S. market range of $155,000–$205,000 per year. ## Core skills Skills and capabilities most closely associated with this opportunity. [Data engineering](https://jobicy.com/jobs?search_keywords=Data%20engineering.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Dagster](https://jobicy.com/jobs?search_keywords=Dagster.md)[Apache Airflow](https://jobicy.com/jobs?search_keywords=Apache%20Airflow.md)[dbt](https://jobicy.com/jobs?search_keywords=dbt.md)[Snowflake](https://jobicy.com/jobs?search_keywords=Snowflake.md)[PostgreSQL](https://jobicy.com/jobs?search_keywords=PostgreSQL.md)[AWS](https://jobicy.com/jobs?search_keywords=AWS.md)[Amazon S3](https://jobicy.com/jobs?search_keywords=Amazon%20S3.md)[IAM](https://jobicy.com/jobs?search_keywords=IAM.md)[Airbyte](https://jobicy.com/jobs?search_keywords=Airbyte.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[Dimensional modeling](https://jobicy.com/jobs?search_keywords=Dimensional%20modeling.md)[ETL pipelines](https://jobicy.com/jobs?search_keywords=ETL%20pipelines.md)[Data quality](https://jobicy.com/jobs?search_keywords=Data%20quality.md) Sample interview questionsHow would you design a reliable daily Postgres-to-Snowflake pipeline?I would use incremental extraction based on a durable watermark or change indicator, land immutable raw files in S3, and load Snowflake through controlled COPY operations. I would make each stage idempotent, record run metadata, validate row counts and freshness, and provide a tested backfill path. When would you choose a dbt full refresh instead of an incremental model? I would use a full refresh when model logic or source history has changed in a way that invalidates existing records, or when the model is small enough that rebuilding is safer. For large stable datasets, I would use an incremental strategy with clear unique keys, late-arriving-data handling, and periodic reconciliation. How do you model slowly changing dimensions in an analytics warehouse? I first define the business grain and the reporting need for historical accuracy. For Type 2 behavior, I maintain surrogate keys plus effective start and end timestamps and a current-record indicator, then ensure facts join to the correct historical dimension version. How would you troubleshoot a failed orchestrated pipeline caused by IAM or AWS infrastructure issues? I would inspect orchestrator logs, task-role permissions, resource identifiers, and CloudTrail or service logs to isolate the failed access action. After applying the least-privilege correction, I would rerun the smallest safe unit, validate outputs, and document the issue and recovery process in a runbook. What makes a data-engineering pull request effective? An effective PR is narrowly scoped, explains the business and technical intent, includes tests and migration or backfill considerations, and identifies operational risks. I also include clear review guidance, avoid unrelated refactors, and ensure documentation reflects any new pipeline behavior or ownership. Opportunity details ## About this role. ### The Role We Need 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 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 ### Compensation 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. ### Additional Information ### 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: All employees must be able to establish their identity and authorization to work in the United States by completing Form I-9 after an offer of employment. For this role, we are unable to provide employment-based visa sponsorship, now or in the future. Applicants must be authorized to work in the U.S. without requiring sponsorship from PadSplit. Candidates who are U.S. citizens, lawful permanent residents, refugees, asylees, and others with unrestricted work authorization are welcome to apply. PadSplit is an equal opportunity employer and does not discriminate on the basis of citizenship status or national origin. 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