Senior Analytics Engineer

This is a fully remote position, open to applicants in Brazil.

📋 Description

• Design, develop, and sustain scalable data pipelines for the purposes of ingestion, transformation, and delivery.

• Create reusable analytical datasets and data models that support business processes and reporting requirements.

• Develop, optimize, and maintain SQL transformations, business logic, and analytical queries.

• Assess data integration methods across operational systems, databases, data warehouses, and reporting platforms.

• Design and oversee pipeline orchestration, scheduling, dependencies, and processes for failure recovery.

• Implement data modeling and transformation practices to enhance maintainability, performance, and reusability.

• Create validation, reconciliation, and monitoring processes to ensure data completeness, consistency, and accuracy.

• Investigate and resolve issues related to data quality, pipeline failures, and discrepancies across systems.

• Establish reliable business metrics, standardized definitions, and documented transformation logic.

• Enhance data traceability, auditability, reproducibility, freshness, performance, and operational reliability.

• Support appropriate data access controls, security measures, and governance standards.

• Collaborate with business stakeholders to comprehend reporting needs, processes, and analytical requirements.

• Clarify and challenge conflicting definitions, assumptions, business rules, and data requirements.

• Develop and manage datasets that support dashboards, reports, and analytical tools like Metabase.

• Work alongside software engineering and technical teams to enhance data integration and the reliability of source systems.

• Replace repetitive manual reports and data requests with automated, reusable solutions.

• Communicate technical findings, data limitations, and suggested approaches to both technical and non-technical stakeholders.

• Utilize version control, code reviews, testing, and documentation standards.

• Contribute to discussions on data architecture and technical design decisions.

• Enhance the maintainability of pipelines, query performance, and operational support processes.

• Share technical knowledge and contribute to ongoing improvement within the team.

⛳️ Requirements

• A minimum of 5 years of professional experience in analytics engineering, data engineering, business intelligence engineering, or a closely related field.

• Advanced proficiency in SQL, including complex joins, common table expressions, window functions, aggregations, and query optimization.

• Strong understanding of relational databases, analytical data warehouses, and principles of data modeling.

• Experience in designing, developing, or maintaining ETL/ELT pipelines.

• Proven experience in building reusable analytical datasets and implementing intricate business transformations.

• Knowledge of data ingestion, orchestration, scheduling, dependencies, error handling, and data delivery patterns.

• Apply version control, code reviews, testing, and documentation standards to data engineering tasks.

• Participate in discussions on data architecture and technical design decisions.

• Improve pipeline maintainability, query performance, and operational support processes.

• Assist in establishing repeatable engineering practices that promote reliable and scalable analytics.

• Share technical expertise and contribute to continuous team improvement.

• Preferred: Experience with Amazon Redshift and PostgreSQL, including AWS RDS.

• Preferred: Familiarity with Apache Airflow or similar 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 similar business intelligence tools.

• Preferred: Experience in implementing automated data quality checks, source-to-target reconciliation, and pipeline monitoring.

• Preferred: Understanding of data governance, lineage, metadata management, auditability, and access control.

• Preferred: Experience with geospatial, agricultural, scientific, or other complex operational datasets.

• Preferred: Experience in supporting analytical environments where accuracy, reproducibility, and traceability are essential.

🏝️ Benefits

• Remote work arrangement.