Job Duties and Responsibilities

Candidates must be willing to participate in at least one in-person interview, which may include a live whiteboarding or technical assessment session.

You will address the challenge of transforming complex, fragmented data requirements into scalable, production-ready AI infrastructure using advanced Databricks and Spark architectures. By implementing rigorous software engineering disciplines and automated CI/CD workflows, you will eliminate bottlenecks in data delivery and ensure the reliability of mission-critical pipelines. Your role is pivotal in harmonizing cross-functional goals with high-quality code standards to drive the next generation of our data platform's evolution.

What Success Looks Like (Objectives)

• Deliver scalable, high-performance data pipelines using Databricks and Spark that meet rigorous departmental OKRs for performance and cost-efficiency
• Build fully automated CI/CD workflows within Gitlab to reduce deployment friction and ensure 100% version-controlled data infrastructure
• Apply SOLID engineering principles and modular design to create a reusable testing framework that guarantees data pipeline stability and quality
• Foster a culture of excellence by leading technical code reviews and mentoring peers to elevate the team's overall software engineering maturity
• Integrate AI-driven automation tools to proactively monitor pipeline health and optimize resource allocation across the AWS ecosystem
• Facilitate seamless collaboration between Data Science and Product teams to transform experimental models into fault-tolerant production solutions

Skills, Experience and Requirements

Core Skills and Competencies (What you'll bring)

• Advanced proficiency in Python and SQL alongside a deep understanding of distributed systems architecture and modern data patterns
• Expertise in Databricks, Spark, and Delta Lake orchestration to manage large-scale, high-velocity data environments
• A strong foundation in DevOps methodologies, specifically regarding infrastructure-as-code and containerization using Docker or Kubernetes
• AI Application literacy, with the ability to leverage machine learning libraries and NLP frameworks to enhance data processing capabilities
• Proven capability in applying design patterns and testing frameworks to ensure the integrity of complex software ecosystems
• Critical experience in building and managing highly available, fault-tolerant systems within an enterprise AWS environment
• Background in handling sensitive data and maintaining strict security protocols

Minimum Requirements

• Minimum Education: Bachelor's Degree in Computer Science, Electrical Engineering, or a related field
• Minimum Experience: 3+ years of experience in data engineering
• Required Technical Skills: Must have at least 3+ years of experience with:
  
  • Python and SQL
  • AI Platforms - Databricks and Spark (including Delta Lake)
  • CI/CD pipelines and Gitlab workflows

Visa sponsorship not available for this role

Salary Ranges

Compensation: $100,980.00/Year - $136,625.00/Year
Benefits

We offer versatile health perks, including flexible spending accounts, HSA, a 401(k) Plan with company match, ESPP, career opportunities, and a flexible time away plan; all benefits can be viewed 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 based on work location.

The posting will be active for a minimum of 3 days. The active posting will continue to extend by 3 days until the position is filled.