Senior Analytics Engineer

• Design, develop, and maintain scalable data pipelines for ingestion, transformation, and delivery
• Build reusable analytical datasets and data models supporting business processes and reporting
• Develop, optimize, and maintain SQL transformations, business logic, and analytical queries
• Evaluate data integration approaches across operational systems, databases, data warehouses, and reporting platforms
• Design and maintain pipeline orchestration, scheduling, dependencies, and failure recovery processes
• Apply data modeling and transformation practices to improve maintainability, performance, and reuse
• Develop validation, reconciliation, and monitoring processes for data completeness, consistency, and accuracy
• Investigate and resolve data quality issues, pipeline failures, and cross-system discrepancies
• Establish trusted business metrics, standardized definitions, and documented transformation logic
• Improve data traceability, auditability, reproducibility, freshness, performance, and operational reliability
• Support appropriate data access controls, security practices, and governance standards
• Work with business stakeholders to understand reporting needs, processes, and analytical requirements
• Clarify and challenge conflicting definitions, assumptions, business rules, and data requirements
• Develop and maintain datasets supporting dashboards, reports, and analytical tools such as Metabase
• Collaborate with software engineering and technical teams to improve data integration and source-system reliability
• Replace recurring manual reports and data requests with automated, reusable solutions
• Communicate technical findings, data limitations, and recommended approaches to technical and non-technical stakeholders
• Apply version control, code reviews, testing, and documentation standards
• Contribute to data architecture discussions and technical design decisions
• Improve pipeline maintainability, query performance, and operational support processes
• Share technical knowledge and contribute to continuous improvement across the team

• 5+ years of professional experience in analytics engineering, data engineering, business intelligence engineering, or a closely related discipline
• Advanced SQL proficiency, including complex joins, common table expressions, window functions, aggregations, and query optimization
• Strong understanding of relational databases, analytical data warehouses, and data modeling principles
• Experience designing, developing, or maintaining ETL/ELT pipelines
• Experience building reusable analytical datasets and implementing complex business transformations
• Understanding of data ingestion, orchestration, scheduling, dependencies, error handling, and data delivery patterns
• Apply version control, code reviews, testing, and documentation standards to data engineering work
• Contribute to data architecture discussions and technical design decisions
• Improve pipeline maintainability, query performance, and operational support processes
• Help establish repeatable engineering practices that support reliable and scalable analytics
• Share technical knowledge and contribute to continuous improvement across the team
• Preferred: Experience with Amazon Redshift and PostgreSQL, including AWS RDS
• Preferred: Experience with Apache Airflow or comparable orchestration platforms
• Preferred: Hands-on experience with AWS data services such as S3, Lambda, Glue, or Database Migration Service (DMS)
• Preferred: Proficiency in Python for data processing, automation, or pipeline development
• Preferred: Experience with Metabase or comparable business intelligence platforms
• Preferred: Experience implementing automated data quality checks, source-to-target reconciliation, and pipeline monitoring
• Preferred: Familiarity with data governance, lineage, metadata management, auditability, and access control
• Preferred: Experience with geospatial, agricultural, scientific, or other complex operational datasets
• Preferred: Experience supporting analytical environments where accuracy, reproducibility, and traceability are critical

• Remote work arrangement