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.