AI Data Engineer II

EchoStar•Denver, CO

8d•Onsite

About The Position

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.

Requirements

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
Bachelor’s Degree in Computer Science, Electrical Engineering, or a related field
3+ years of experience in data engineering
Minimum 3+ years of experience with Python and SQL
Minimum 3+ years of experience with AI Platforms - Databricks and Spark (including Delta Lake)
Minimum 3+ years of experience with CI/CD pipelines and Gitlab workflows

Responsibilities

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