Agentic Data Engineer II

Company Summary

Echo Star is reimagining the future of connectivity. Our business reach spans satellite television service, live-streaming and on-demand programming, smart home installation services, mobile plans and products.

Today, our brands include Boost Mobile, DISH TV, Gen Mobile, Hughes and Sling TV.

Our Retail Wireless team, serving our Boost Mobile and Gen Mobile brands, is redefining consumer expectations through new platforms, new business models and new ways of thinking. Equipped with a passion for change and the power to drive it, we continue to push boundaries and be a disruptive force in the market.

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

Organizations face growing complexities in managing and scaling data infrastructure for modern analytics and emerging AI initiatives. This role addresses these challenges by developing reliable data pipelines and maintaining robust workflows optimized for machine learning models and large language model consumption. Engineering teams must continuously balance technical performance with practical business outcomes to control cloud expenditure and avoid unnecessary computational overhead.

Overcoming these hurdles requires implementing effective data observability, ensuring data governance, and collaborating cross-functionally to align operational data structures with business goals.

What Success Looks Like (Objectives):

• Implement scalable data pipeline designs that optimize current infrastructure performance and improve resource utilization

• Apply cost-effective engineering practices to prioritize development tasks that enhance operational efficiency and data deliverability

• Build and maintain reliable data pipelines tailored for large language model (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

Core Skills and Competencies (What you’ll bring):

• Practical experience developing and deploying production-grade data pipelines within cloud environments

• Demonstrated ability to evaluate technical stacks and optimize existing data processing workflows before introducing new tools

• Ability to build new AI Agents to support business operations

• Applied AI skills, specifically in 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 in diagnosing system bottlenecks and resolving issues within distributed data platforms

Additional Qualifications

• Master’s degree in Computer Science, Data Engineering, Artificial Intelligence, or a closely related technical field

Minimum Requirements

• Minimum Education: Bachelor’s Degree in Computer Science or a related technical field
• Minimum Experience: 2 years of experience in data engineering
• Required Technical
  
  Skills:
  
  Must have at least 1 year of experience with:

  • Databricks, including Spark optimization and Delta Lake architecture

  • Snowflake, including building and optimizing relational data models

  • Python and SQL for data manipulation

Visa sponsorship not available for this position

Compensation: $83,160.00/Year - $/Year

We offer versatile health perks, including flexible spending…