Data Engineer (2–6 Years)

Remote, USA • Full-time • Posted 2026-05-31

About The Role

The role designs, builds, and maintains the data pipelines and infrastructure that move data from raw sources to clean, analytical-ready tables clients rely on daily.

You will work across the modern data stack - Python, Airflow, dbt, Snowflake, Kafka, and Spark - and will be expected to own your pipelines end-to-end.

Build and maintain ETL/ELT pipelines using Airflow or Prefect to move and transform data from operational databases, APIs, event streams, and SaaS sources
Develop dbt data models following dimensional modeling and OBT patterns; implement dbt tests, documentation, and data lineage
Administer and optimize Snowflake or Databricks environments: clustering keys, warehouse sizing, cost management, and query performance
Build real-time streaming pipelines using Kafka and Spark Structured Streaming for event-driven data products
Implement data quality monitoring using Great Expectations or dbt tests; build alerting pipelines for data SLA violations
Collaborate with analytics engineers, data scientists, and ML engineers to ensure their data access and quality requirements are met
Write clear technical documentation for pipeline architecture, data dictionaries, and on-call runbooks

2–6 years of data engineering experience with evidence of production pipeline ownership
Python proficiency: pandas, PySpark, and scripting for data processing and orchestration
Airflow or Prefect for workflow orchestration; hands-on experience designing DAGs in production
SQL expertise: complex queries, window functions, performance tuning across Snowflake, BigQuery, or Redshift
dbt experience (any version): model development, testing, and documentation
Familiarity with at least one cloud data platform: Snowflake, BigQuery, or Databricks
Bonus: Kafka/Flink for streaming, Delta Lake, dbt Semantic Layer, or Fivetran/Airbyte connector management

Location