Business Systems Analyst

AFL; Thurso; On-site; GBP 45,000 - 65,000; Full time

Job summary

AFL is seeking a Business Systems Analyst - Data Engineering & Operations to design and deliver data-driven solutions that improve operational efficiency, reporting accuracy, and business performance across the Energy business. The role focuses on building and structuring data, developing BI solutions, and enabling a scalable foundation for decision making.

You will be hands-on in developing data and reporting solutions, not solely defining requirements, and will work across Operations, Product,

Qualifications

• Bachelor's degree in a relevant field.
• 3-6+ years of experience in data analytics or data engineering-adjacent roles.
• Demonstrated experience building data models, BI solutions, or automated workflows.
• Experience working with ERP/CRM systems and understanding data flows.

Responsibilities

• Design scalable data models to support BI and reporting needs.
• Build and maintain Power BI dashboards using advanced DAX and SQL.
• Identify and resolve data fragmentation to create a single source of truth.
• Develop data pipelines or transformations to improve data accessibility and consistency.
• Collaborate with stakeholders across ops, product, engineering, finance, and IT to define requirements.

Skills

Analytical thinking

Problem solving

Communication

Education

Bachelor's degree in Data Analytics or related field

Tools

Power BI

SQL

Python

Power Automate

Job description

Job Summary

The Business Systems Analyst - Data Engineering & Operations is responsible for designing and delivering data-driven solutions that improve operational efficiency, reporting accuracy, and business performance across the Energy business.

The Business Systems Analyst - Data Engineering & Operations is responsible for designing and delivering data-driven solutions that improve operational efficiency, reporting accuracy, and business performance across the Energy business. This role is heavily focused on building and structuring data, developing business intelligence solutions, and enabling a scalable foundation for decision making. Operating at the intersection of Business Operations and IT, this position requires strong technical capability alongside the ability to translate business needs—including operational and financial performance drivers—into structured, system-driven solutions. The role owns initiatives end-to-end - from identifying data and process gaps through development and implementation of solutions - and is expected to be hands-on in developing data and reporting solutions, not solely defining requirements. Over time, this role will develop a strong understanding of how the business operates, enabling more effective solution design and contribution to business decision‑making.

Key Responsibilities

• Design and develop well-structured, scalable data models designed for reusability and performance to support business intelligence and reporting needs
• Build and maintain Power BI dashboards and reporting solutions using advanced DAX and SQL
• Identify and resolve data fragmentation, driving toward a consistent and reliable source of truth
• Develop and support data pipelines or structured data transformations to improve accessibility and consistency of data
• Partner with stakeholders across Business Operations, Product Management, Engineering, Finance, and IT to define technical requirements and deliver solutions
• Act as a technical liaison between business teams and IT, ensuring alignment on system design, data architecture, and integration
• Identify and implement opportunities to automate workflows and reduce manual processes using Python, Power Automate, or system-based solutions
• Leverage AI-enabled tools and workflows to accelerate data analysis, solution development, and process improvement
• Translate data outputs into clear insights and recommendations that support business decision making
• Develop an understanding of how the business operates across pricing, operations, and financial performance
• Engage with cross-functional teams to understand underlying business logic, assumptions, and decision drivers

Technical Expectations

This role requires hands-on development experience in SQL and Power BI (DAX). Candidates without direct experience building data models and dashboards will not be considered.

• Strong proficiency in SQL (complex queries, joins, data transformation, and dataset structuring)
• Advanced experience with Power BI, including data modeling and writing complex DAX measures
• Solid understanding of relational data models, data structures, and system integrations
• Experience building or supporting data pipelines, ETL processes, or structured data workflows
• Working knowledge of Python, Power Automate, or similar tools for automation and data processing
• Familiarity with applying AI tools to enhance productivity and solution development

Key Success Traits

• Strong technical problem solver who can translate business needs and operational context into data and system solutions
• High ownership mindset; able to independently drive solutions from concept through implementation
• Ability to operate effectively across business and technical domains
• Curiosity and learning mindset with interest in understanding how the business operates, including operational and financial drivers
• Ability to connect data, process, and business impact
• Curiosity and adaptability to apply AI and emerging technologies
• Detail-oriented with focus on data integrity, consistency, and scalability

Qualifications

• Bachelor’s degree in Data Analytics, Information Systems, Engineering, Computer Science, or related field
• 3-6+ years of experience in data analytics, business systems, or data engineering-adjacent roles
• Demonstrated experience building data models, BI solutions, or automated workflows
• Experience working with ERP/CRM systems and understanding data flows
• Background in manufacturing, industrial, or operations-driven environments preferred

Measures of Success

• Establishment of reliable, scalable data sources and models
• Reduction of data inconsistencies and fragmentation
• Increased adoption and effectiveness of BI tools
• Reduction in manual processes through automation
• Improved visibility into operational and financial performance