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# Senior Data Warehouse Engineer

- **Company:** ECHOSTAR
- **Location:** Greenwood Village, CO, United States
- **Experience:** Expert
- **Salary:** $96,250.0 - $137,500.0
- **Contract:** Permanent contract
- **Skills:** Third Normal Form, Airflow, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Data Analysis, Cloud Computing, Cloud Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Warehousing, DevOps, Identity and Access Management, Python (Programming Language), Scrum Methodology, Shell Script, Software Engineering, SQL Databases, Enterprise Data Management, Snowflake, Reliability of Systems, Generative AI, Amazon Virtual Private Cloud (VPC), Gitlab, Git, Cloudformation, Data Lakes, Infrastructure Automation Frameworks, Information Technology, AWS Data Analytics, Google Gemini, Cloudwatch, Terraform, Data Pipelines, Amazon Redshift, Databricks, Control M
- **Published:** October 16, 2026
- **Apply:** https://dejobs.org/x/x/DB7D93AF6B7241F3B38775714CF15853/job/

## About the Role

* Critical experience designing, developing, and engineering large-scale Enterprise Data Warehouses (EDW) and modern data lake architectures
 * Advanced proficiency in SQL, Python, and Shell scripting for building and deploying production-grade data pipelines
 * Deep expertise in AWS data services and cloud infrastructure, including IAM, VPC, CloudWatch, Redshift, and S3
 * Strong ability in relational and analytical data modeling utilizing Dimensional (Kimball) and 3NF structures
 * High proficiency in DevOps best practices, version control using Git/GitLab, and IaC tools such as Terraform or AWS CloudFormation
 * AI Literacy and Application skills to evaluate modern technologies like Generative AI tools (Amazon Q, Gemini, Databricks Genie Rooms) to accelerate development workflows, * Minimum Education: Bachelor's degree in Computer Science, Software Engineering, or a related technical field
 * Minimum Experience: 4+ years of professional experience in Enterprise Data Warehousing and data lake engineering
 * Required Technical Skills: Must have at least 3 years of experience with:
 * SQL, Python, and Shell scripting
 * AWS Cloud Data Platform (S3, Redshift, EC2, Glue), Snowflake or Databricks ecosystems

## Description

Candidates must be willing to participate in at least one in-person interview.

This role addresses the complex challenges of scaling enterprise data infrastructure and maintaining peak system reliability across massive ecosystems. The position focuses on eliminating performance bottlenecks, streamlining ETL/ELT workflows, and maintaining strict data quality standards. Additionally, modernizing data pipeline engineering with cloud-native architectures and leveraging artificial intelligence will drive continuous operational excellence.

What Success Looks Like (Objectives):

 * Monitor and provide expert-level operational support for large enterprise data ecosystems using Snowflake and Databricks while resolving ETL/ELT bottlenecks
 * Architect, maintain, and optimize scalable batch and streaming pipelines on AWS using clean, production-grade Python code
 * Design, automate, and manage complex enterprise data workflows and job schedules using Apache Airflow and Control-M
 * Champion Infrastructure-as-Code (IaC), manage CI/CD pipelines via GitLab, and integrate Generative AI tools like Amazon Q to drive operational efficiency
 * Lead incident management, root cause analysis meetings, and operational metrics development to continuously improve production systems
 * Collaborate within Agile/Scrum frameworks alongside Data Scientists, Analysts, and DevOps teams to execute complex data transformation logic