Senior Data Analyst - Data Management & BI

EchoStar•Issaquah, WA

1d•Onsite

About The Position

The Enterprise Data Management Environment requires modernization and design optimization to support evolving business initiatives. Fragmented data ingestion applications and complex production pipelines demand scalable, high-performance architecture to ensure reliable analytical outputs. This role addresses these challenges by developing advanced ETL processes, optimizing cloud infrastructure, and evaluating emerging AI tools to continuously improve warehouse workflows.

Requirements

Bachelor's degree in Data Science, Computer Science, or a closely related quantitative field
Strong skills in designing, optimizing, and modernizing data architectures within an Enterprise Data Management Environment
Competency in building scalable ETL processes, data ingestion systems, and streaming applications via AWS, Snowflake, and Redshift
Advanced proficiency in SQL optimization, Python automation, and Linux/Unix shell scripting for robust data pipeline maintenance
AI Literacy and AI Application skills to proactively evaluate and integrate emerging AI tools into existing warehouse workflows
Critical thinking and collaborative problem-solving abilities to resolve complex IT issues and deliver sound data insights across cross-functional teams
Minimum 1 year of experience with SQL
Minimum 1 year of experience with Airflow or Control-M orchestration tools
Minimum 1 year of experience with RDBMS and modern Data Warehousing concepts

Nice To Haves

Experience working within Agile teams to deliver rapid, high-quality data solutions
Possession of a Master’s degree in Data Science, Computer Science, or a closely related quantitative field

Responsibilities

Design ETL processes, data ingestion mechanisms, and streaming applications utilizing cloud services, SQL, Python, Snowflake, and AWS Redshift
Integrate data extracts, loads, and reports seamlessly with business intelligence and analytical applications
Maintain core data pipelines while evaluating and resolving critical production issues to ensure minimal downtime
Collaborate closely with analysts and engineers by leveraging modern orchestration and ETL tools like Airflow and Databricks
Optimize complex SQL queries to maximize system performance and manage the underlying cloud infrastructure effectively
Explore and evaluate new technologies with AI tools to continuously improve upon existing Data Warehouse processes