What Success Looks Like (Objectives):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
Core Skills, Competencies, and Key Experiences (What you'll bring)Core Skills and Competencies (What you'll bring):- 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 Requirements:- 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
Visa sponsorship not available for this role
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Salary RangeCompensation: $96,250.00/Year - $137,500.00/Year
About Our DepartmentOur Technology teams challenge the status quo and reimagine capabilities across industries. Whether through research and development, technology innovation or solution engineering, our team members play a vital role in connecting consumers with the products and platforms of tomorrow.
BenefitsWe offer meaningful health perks, including flexible spending accounts, HSA, a 401(k) plan with company match, employee stock purchase plan, career opportunities, and a flexible time away plan. Review a benefits summary here: EchoStar Benefits
The base pay range shown is a guideline. Individual total compensation will vary based on factors such as qualifications, skill level, and competencies. Compensation is based on the role's location and is subject to change.
