Agentic Data Engineer II

EchoStar is addressing the growing complexity of scaling data infrastructure for modern analytics and emerging AI initiatives. In this on-site role in Littleton, CO, you will develop production-grade data pipelines and workflows built to support machine learning consumption and large language model (LLM) use cases, with emphasis on observability, governance, and cost-effective operations.

You will help engineering teams balance technical performance with practical business outcomes, building pipelines for LLM feature stores and AI application workflows while adding automated monitoring and self-healing mechanisms to reduce workflow failures and corrective overhead.

What You’ll Do

• Implement scalable data pipeline designs that optimize existing infrastructure performance and improve resource utilization.
• Use cost-effective engineering practices to prioritize work that improves operational efficiency and data deliverability.
• Build and maintain reliable data pipelines tailored for LLM feature stores and AI application workflows.
• Develop automated monitoring and self-healing mechanisms to detect workflow failures and trigger corrective actions.
• Integrate observability tools to track data freshness, lineage, and baseline performance metrics across production environments.
• Structure complex datasets into standard semantic layers to enable low-latency access for downstream AI systems and analytical queries.
• Participate in at least one in-person interview.

Minimum Requirements

• Practical experience developing and deploying production-grade data pipelines within cloud environments.
• Experience evaluating technical stacks and optimizing existing data processing workflows before introducing new tools.
• Ability to build new AI Agents to support business operations.
• Applied AI skills, including integrating large language models and vector datasets into enterprise data pipelines.
• Strong proficiency in Python and SQL for data transformation, scripting, and API integration.
• Solid skills in Git version control, containerization tools, and CI/CD deployment pipelines.
• Proven troubleshooting capabilities for diagnosing system bottlenecks and resolving issues within distributed data platforms.
• 2+ years of experience in data engineering.
• Education: Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a closely related technical field.
• Bachelor’s Degree: Computer Science or related technical field.
• No visa sponsorship available for this position.
• At least 1 year experience with Databricks, including Spark optimization and Delta Lake architecture.
• At least 1 year experience with Snowflake, including building and optimizing relational data models.
• At least 1 year experience with Python and SQL for data manipulation.

Technologies

• Python, SQL
• Git, CI/CD
• Databricks, Spark, Delta Lake, Delta Lake architecture
• Snowflake
• LLM, vector datasets
• Git version control, containerization tools

Compensation and Location

• Location: Littleton, CO (onsite)
• Base pay range: USD 83,160 - 118,800 per year
• The base pay range shown is a guideline; individual total compensation varies based on qualifications, skill level, and competencies, and is based on the role’s location and subject to change based on work location.
• Salary range also listed as: USD $83,160.00 - $118,800.00 / Year

Benefits

• Flexible spending accounts
• HSA
• A 401(k) Plan with company match
• ESPP
• Career opportunities
• Flexible time away plan