Senior Data Warehouse Engineer

EchoStar • Centennial, CO

7d

About The Position

The Senior Data Warehouse Engineer addresses challenges in scaling enterprise data infrastructure and maintaining reliability across large data ecosystems. The role focuses on eliminating performance bottlenecks, streamlining ETL/ELT workflows, maintaining data quality, and modernizing pipeline engineering with cloud-native architectures and artificial intelligence.

The engineer provides operational support for enterprise data ecosystems using Snowflake and Databricks; builds and optimizes batch and streaming pipelines on AWS; manages workflows and schedules; and advances infrastructure-as-code, CI/CD, and the use of generative AI tools. The position also leads incident management and root-cause analysis, develops operational metrics, and collaborates with Data Scientists, Analysts, and DevOps teams in Agile/Scrum settings.

Candidates must be willing to participate in at least one in-person interview. Visa sponsorship is not available. The role requires a pre-employment screen, which may include a drug test and DMV check.

Requirements

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
• At least 4 years of professional experience in enterprise data warehousing and data lake engineering.
• At least 3 years of experience with SQL, Python, and Shell scripting.
• At least 3 years of experience with the AWS Cloud Data Platform, including S3, Redshift, EC2, and Glue, and with Snowflake or Databricks ecosystems.
• Critical experience designing, developing, and engineering large-scale Enterprise Data Warehouses 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 using Dimensional (Kimball) and 3NF structures.
• Willingness to participate in at least one in-person interview.

Nice To Haves

• High proficiency in DevOps best practices, Git/GitLab version control, and IaC tools such as Terraform or AWS CloudFormation.
• AI literacy and the ability to evaluate generative AI tools such as Amazon Q, Gemini, and Databricks Genie Rooms to accelerate development workflows.

Responsibilities

• Monitor and provide expert-level operational support for large enterprise data ecosystems using Snowflake and Databricks, resolving ETL/ELT bottlenecks.
• Architect, maintain, and optimize scalable batch and streaming pipelines on AWS using production-grade Python.
• 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 such as Amazon Q to improve operational efficiency.
• Lead incident management and root-cause analysis meetings, and develop operational metrics to improve production systems.
• Collaborate in Agile/Scrum frameworks with Data Scientists, Analysts, and DevOps teams to execute complex data transformation logic.

Benefits

• Flexible spending accounts
• Health Savings Account (HSA)
• 401(k) plan with company match
• Employee stock purchase plan
• Career opportunities
• Flexible time away plan

What This Job Offers

• Job Type: Full-time
• Career Level: Senior