Senior Analytics Engineer

About the Role

We are seeking a Senior Analytics Engineer to design, build, and maintain the data pipelines, analytical models, and trusted datasets that support business operations, reporting, and decision-making.

This is a hands-on technical role that combines data engineering, analytics engineering, and business problem-solving. You will work across operational databases, data warehouses, and reporting systems to transform complex data into reliable, reusable information.

Working closely with business stakeholders, software engineers, and data teams, you will help define business metrics, establish consistent data definitions, improve data quality, and reduce reliance on manual reporting processes.

The ideal candidate understands not only how to move and transform data, but also how to evaluate where data should reside, how it should be modeled, and how its accuracy and reliability can be maintained.

You will be expected to independently investigate complex data challenges, challenge unclear requirements, and recommend sustainable solutions that support both current operational needs and future growth.

Key Responsibilities

Data Engineering & Analytics Development

• Design, develop, and maintain scalable data pipelines for ingestion, transformation, and delivery.
• Build reusable analytical datasets and data models that support multiple business processes and reporting needs.
• 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 appropriate data modeling and transformation practices to improve maintainability, performance, and reuse.
• Data Quality, Reliability & Governance
• Develop validation, reconciliation, and monitoring processes to ensure data completeness, consistency, and accuracy.
• Investigate and resolve data quality issues, pipeline failures, and discrepancies between systems.
• Establish and maintain trusted business metrics, standardized definitions, and documented transformation logic.
• Improve data traceability, auditability, and reproducibility across analytical processes.
• Identify opportunities to improve data freshness, pipeline performance, and operational reliability.
• Support appropriate data access controls, security practices, and governance standards.

Business Analytics & Collaboration

• Work directly with business stakeholders to understand reporting needs, business processes, and analytical requirements.
• Clarify and challenge conflicting definitions, assumptions, business rules, and data requirements before implementation.
• Develop and maintain datasets that support dashboards, reports, and analytical tools such as Metabase.
  Collaborate with software engineering and other technical teams to improve data integration and source-system reliability.
• Identify opportunities to replace recurring manual reports and data requests with automated, reusable solutions.
• Communicate technical findings, data limitations, and recommended approaches clearly to technical and non-technical stakeholders.

Engineering Practices & Continuous Improvement

• 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.

Required Qualifications

• 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 Qualifications

• Experience with Amazon Redshift and PostgreSQL, including AWS RDS.
• Experience with Apache Airflow or comparable orchestration platforms.
• Hands-on experience with AWS data services such as S3, Lambda, Glue, or Database Migration Service (DMS).
• Proficiency in Python for data processing, automation, or pipeline development.
• Experience with Metabase or comparable business intelligence platforms.
• Experience implementing automated data quality checks, source-to-target reconciliation, and pipeline monitoring.
• Familiarity with data governance, lineage, metadata management, auditability, and access control.
• Experience with geospatial, agricultural, scientific, or other complex operational datasets.
• Experience supporting analytical environments where accuracy, reproducibility, and traceability are critical.

What success looks like this role

• Deliver trusted, reusable analytical datasets that support business reporting and decision-making.

• Improve consistency and accuracy across data originating from multiple operational systems.

• Reduce manual reporting effort through reliable, automated data pipelines.

• Establish clear business definitions and maintainable transformation logic.

• Improve data pipeline reliability, performance, monitoring, and recovery.

• Strengthen collaboration between business stakeholders, software engineering, and data teams.

• Contribute to a scalable and well-documented data foundation that supports future business growth.

Originally posted on Himalayas