Remote Senior Data Engineer: Pipelines, Snowflake & dbt

PadSplit, Inc; Atlanta (GA); Remote; USD 140,000 - 190,000; Full time

Benefits offered by this job

Fully remote position

Equity incentive plan

Company-wide bonus

National medical/dental/vision

Life insurance

Unlimited PTO

401(k)

Parental leave

Job summary

PadSplit, Inc. is seeking a hands-on Sr. Data Engineer to build and maintain ingestion and transformation pipelines across Dagster (or Airflow), dbt, and Snowflake.

You will work with Python, Airbyte, and AWS to ensure reliable daily data loads and critical paths from Postgres to Snowflake and dbt. The role emphasizes PR-driven development, code reviews, and clear runbooks, with a focus on reliability and scalable data models.

Qualifications

• Experience building ingestion and transformation pipelines with Dagster/Airflow.
• Strong dbt modeling, testing, and documentation capabilities.
• Hands-on Python data workflows with AWS familiarity.

Responsibilities

• PR-driven shipping of Dagster jobs, dbt models, tests, and docs.
• Tuning infra with Terraform for secrets and environment vars.
• Build and debug Python pipelines for REST/API, Postgres, Parquet, Snowflake.
• Manage Airbyte connections and troubleshooting.
• Monitor production runs; investigate IAM, OOM, Spot issues.
• Run backfills and incremental catch-ups with clear rationale.
• Collaborate on dim/fct/x_fct designs and data quality.
• Participate in code reviews and write concise runbooks.

Skills

Dimensional modeling

Data pipelines

PR-driven reviews

Problem solving

Communication

Tools

Dagster

Airflow

dbt

Snowflake

Airbyte

Python

AWS

Job description

PadSplit, Inc. is seeking a hands-on Sr. Data Engineer to build and maintain ingestion and transformation pipelines across Dagster (or Airflow), dbt, and Snowflake.

You will work with Python, Airbyte, and AWS to ensure reliable daily data loads and critical paths from Postgres to Snowflake and dbt. The role emphasizes PR-driven development, code reviews, and clear runbooks, with a focus on reliability and scalable data models.