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Senior Analytics Engineer
Agoro Carbon AllianceCore Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and maintaining scalable data pipelines, optimizing SQL transformations, and implementing data governance practices. Proficient in collaborating with stakeholders to deliver reliable analytical datasets and improve data quality and performance.
Highest-signal resume keywords
Advanced SQL ProficiencyETL/ELT Pipeline DevelopmentData Modeling PrinciplesData Governance PracticesExperience with AWS Data Services
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- 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
Requirements
What you’ll need- 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
Benefits
Comp & perks- Remote work arrangement
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
SQL TransformationsData ModelingData IngestionData Quality ChecksAutomated Reporting SolutionsComplex Business TransformationsPipeline OrchestrationQuery OptimizationData ReconciliationData Monitoring
Soft Skills
CollaborationCommunicationProblem-SolvingContinuous ImprovementStakeholder Engagement
Tools & Technologies
Amazon RedshiftPostgreSQLApache AirflowMetabaseAWS S3AWS LambdaAWS GlueDatabase Migration Service
Industry Keywords
Analytics EngineeringData EngineeringBusiness Intelligence EngineeringData GovernanceOperational Reliability