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# Data Engineer

- **Company:** ApTask
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Information Engineering, Document-Oriented Databases, Performance Tuning, Standard Sql, Data Streaming, Snowflake, Apache Spark, Git, Data Lakes, Pyspark, Terraform, Data Pipelines, Databricks
- **Published:** October 6, 2026
- **Apply:** https://www2.jobdiva.com/portal/?a=4ujdnwqsdebu7m13em5f0pt5dw80o500d7dv9cbq5ebzngb7yk0n43mjtefnbx0d&compid=0/jobs/29419513#/jobs/29419513

## About the Role

* 5-7 + years of data engineering experience
 * Strong PySpark and SQL skills
 * Hands-on experience with Databricks (Delta Lake, jobs, notebooks/workflows)
 * Hands-on experience with Snowflake (schemas, performance tuning, access control)
 * Production experience with Airflow
 * Solid grounding in data modeling (star/snowflake schemas, slowly changing dimensions, normalization trade-offs)
 * Experience with Git-based CI/CD for data code

Nice to have

 * Experience integrating unstructured or semi-structured document data into pipelines
 * Infrastructure-as-code (Terraform) and cloud experience ([Azure/AWS])
 * Streaming or CDC experience

## Description

We're looking for a Data Engineer to design, build, and operate the data pipelines and models that power analytics and downstream applications. You'll work across our Databricks and Snowflake platforms and own pipelines from ingestion through curated, consumption-ready datasets.
Responsibilities

 * Build and maintain batch and incremental data pipelines using PySpark on Databricks
 * Orchestrate and monitor workflows in Airflow (DAG design, scheduling, retries, alerting)
 * Design dimensional and domain data models in Snowflake to support reporting and integrations
 * Develop integration pipelines that move data between source systems, the lakehouse, and consuming applications
 * Implement data quality checks, lineage, and documentation
 * Tune performance and cost across Spark clusters and Snowflake warehouses
 * Partner with analysts, application teams, and platform engineers to translate requirements into reliable datasets