Senior Data Analytics and Operations Engineer

Responsibilities

• Monitor and support daily ELT pipelines and reporting processes.
• Design, document, and maintain data warehouse models as source systems evolve. 
• Develop and maintain semantic-layer and business intelligence data models. 
• Support month-end reporting and recurring business processes. 
• Improve and prepare data assets for AI-enabled workflows and automation initiatives. 
• Design data transformations to improve data quality, consistency, accessibility, and usability. 
• Troubleshoot data issues and support analytics requests across the organization. 
• Serve as the primary technical resource for analytics infrastructure and reporting systems. 
• Prepare internal metrics, analytics, and leadership reporting. 
• Collaborate with stakeholders on data-driven initiatives and continuous improvements. 

Requirements

• 6+ years of professional experience in analytics engineering, data engineering, business intelligence, analytics, or related fields
• Proven experience owning analytics and data infrastructure end-to-end, including the ability to make technical and architectural decisions independently.
• Demonstrated ability to operate with a high degree of autonomy and self-sufficiency, identifying problems, defining solutions, and executing them without requiring constant guidance.
• Strong understanding of data warehousing, dimensional modeling, semantic-layer concepts, and modern analytics architectures.
• Extensive experience designing, building, maintaining, and troubleshooting ELT/ETL pipelines in production environments.
• Advanced SQL and data modeling skills, including experience working with complex transformations and business logic.
• Strong hands-on professional experience with dbt, Amazon Redshift, Amazon QuickSight, AWS, Python, Docker, GitHub, and SQL.
• Experience maintaining and evolving existing data platforms, including legacy data cleanup, data reconciliation, and complex financial or business logic.
• Strong problem-solving and troubleshooting skills, with the ability to independently investigate ambiguous data, pipeline, reporting, or infrastructure issues.
• Startup mindset: proactive, adaptable, resourceful, and comfortable working in environments where priorities, systems, and requirements evolve quickly.
• Strong communication, documentation, and stakeholder management skills, including the ability to translate business requirements into scalable technical solutions.
• Advanced English.

• Experience designing or building analytics infrastructure from the ground up.
• Experience with CI/CD workflows for analytics or data engineering systems.
• Experience improving data quality, observability, governance, and reliability across complex data environments.
• Knowledge of data governance practices and experience in healthcare, finance, or other regulated industries.

 

 

 

 

 

 

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