Data Engineer III
GHXOffice Location or Remote - USA$98k – $131kPosted 30 March 2026
Tech Stack
Job Description
This role is not eligible for visa sponsorship
ROLE OVERVIEW
The Data Engineer III (Enterprise BI) designs, builds, and supports data solutions that power enterprise reporting and analytics. You'll bridge the gap between data infrastructure and business insights, partnering with Product, Engineering, Analytics, and business stakeholders to deliver reliable pipelines, optimize data models, and translate complex data into actionable insights. This role requires expertise in BI tools (Tableau, Sigma Computing, SAP Business Objects), SQL, Python, and cloud data platforms (AWS/Snowflake) within an Agile delivery environment.
CORE RESPONSIBILITIES
Design and build ETL/ELT pipelines and dimensional data models using dbt, Airflow, Python, PySpark, and AWS services (S3, Glue, Lambda)
Create executive dashboards and perform complex SQL analysis to drive strategic decisions (Tableau, Sigma, SAP BO)
Optimize SQL queries, data structures, and warehouse resources for performance and cost efficiency at scale (Snowflake, Redshift)
Partner with stakeholders to translate business requirements into self-service analytics capabilities
Implement infrastructure-as-code (CloudFormation/CDK) and contribute to CI/CD automation
Troubleshoot production issues across data pipelines, queries, and APIs; perform root cause analysis
Provide technical mentorship, establish development standards, and drive data engineering best practices
Document solutions and communicate designs to cross-functional teams in Confluence/JIRA
Apply data governance, security, and monitoring/alerting best practices
Leverage AI-assisted development tools (GitHub Copilot, Claude, etc.) to increase productivity and accelerate delivery
REQUIRED QUALIFICATIONS
Education Experience
Bachelor's degree in Computer Science, Data Science, Mathematics, Statistics, or related quantitative field
6+ years of data engineering experience building BI applications and data platforms
5+ years of ETL/ELT development in cloud data warehouses (AWS, Snowflake, Redshift, or similar)
4+ years creating dashboards and visualizations in enterprise BI tools (Tableau, Sigma, SAP BO, Power BI, or Looker)
Proven track record delivering production data solutions in Agile environments (Scrum/Kanban)
Technical Skills
Expert-level SQL and Python proficiency
Proven experience designing dimensional data models (star/snowflake schema) optimized for analytics
Demonstrated SQL optimization and performance tuning in large-scale production environments
Strong business acumen with ability to translate technical solutions into business value
Excellent communication skills for presenting to executive and non-technical audiences
Deep analytical and troubleshooting skills with root cause analysis capabilities
Must be located in the United States (remote position)
PREFERRED QUALIFICATIONS
Cloud Data Engineering
Advanced Snowflake experience (streams, tasks, dynamic tables, Snowpipe, time travel)
Hands-on experience with dbt for analytics engineering and data quality testing
Apache Airflow (or Prefect, Dagster) for workflow orchestration
Deep AWS experience (Glue, Lambda, Step Functions, SNS/SQS, API Gateway, EventBridge)
PySpark for distributed data processing and large-scale transformations
Streaming data platforms (Kafka, AWS Kinesis, Spark Streaming) for real-time analytics
Alteryx Cloud Designer (Trifacta)
Development DevOps
Infrastructure-as-code using CloudFormation or CDK
Modern Angular (17+) for data-driven web applications; AngularJS modernization experience a plus
Version control (Git), CI/CD workflows (GitHub Actions, GitLab CI), and containerization (Docker)
Python data science libraries (pandas, numpy, scipy) and statistical analysis
AI-assisted development tools (Claude Code, GitHub Copilot, OpenAI Codex) and LLM integration
Data Governance Quality
Data governance frameworks and cataloging tools (Alation, Collibra) with metadata management experience
Data quality and obs ... (truncated, view full listing at source)
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