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Key Responsibilities and Required Skills for Business Systems Data Analyst

💰 $80,000 - $120,000

AnalyticsBusiness SystemsDataBIIT

🎯 Role Definition

The Business Systems Data Analyst is a hybrid analyst-engineer role focused on understanding business processes, translating requirements into technical designs, and delivering accurate, actionable analytics. This role owns end-to-end data solutions that support operational systems (ERP/CRM), reporting/BI platforms, and cross-functional analytics initiatives. The analyst collaborates with product owners, finance, sales, operations, IT and data engineering to deliver high-quality data, optimize business system configurations, and produce insights that drive measurable improvements.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst with exposure to ERP/CRM reporting
  • Business Analyst or Systems Analyst with strong data skills
  • Reporting/BI Specialist or Financial Analyst with SQL experience

Advancement To:

  • Senior Business Systems Data Analyst
  • Business Intelligence Lead / Analytics Manager
  • Data Product Manager or Business Systems Manager

Lateral Moves:

  • Data Engineer (with increased engineering focus)
  • CRM/ERP Functional Consultant
  • Operations Analytics or Revenue Operations (RevOps) roles

Core Responsibilities

Primary Functions

  • Design, develop and maintain scalable dashboards and reports in BI platforms (Tableau, Power BI, Looker, etc.) that provide operational visibility and support executive decision-making.
  • Write, optimize and maintain complex SQL queries, stored procedures and views to extract, transform and aggregate data from transactional systems and data warehouses.
  • Lead requirements gathering sessions with cross-functional stakeholders to translate business questions into measurable metrics, data models and technical specifications.
  • Build and maintain ETL/ELT pipelines using tools such as Informatica, Fivetran, dbt or custom Python scripts; ensure pipelines are robust, well-documented and monitorable.
  • Conduct data modeling and design logical and physical schemas that align business concepts with data architecture and reporting needs.
  • Own data quality initiatives: define validation rules, implement automated checks, investigate anomalies and remediate root causes in source systems or ingestion processes.
  • Implement and maintain master data management (MDM) and reference data standards for critical entities (customers, products, accounts) to ensure consistency across systems.
  • Integrate business systems (ERP, CRM, e-commerce platforms) via APIs or middleware and manage synchronization logic, field mappings and transformation rules.
  • Partner with Finance, Sales, Operations and Product teams to translate KPIs into executable reporting frameworks and deliver regular performance reviews.
  • Configure and optimize business system settings, workflows and automation to improve user experience and operational efficiency (e.g., Salesforce flows, ERP business rules).
  • Create reproducible analytics workflows and documentation so business users and analysts can self-serve with confidence and governance.
  • Perform root-cause analysis for data discrepancies between source systems, staging layers and reporting outputs; communicate findings and action plans to stakeholders.
  • Implement row-level security and access controls in BI tools and data stores to protect sensitive information while enabling broad insights.
  • Analyze process and system performance metrics to identify bottlenecks and recommend improvements in upstream systems or data architectures.
  • Collaborate with data engineering to scope and prioritize platform enhancements, data product backfills, and architecture changes that unlock new analytics capabilities.
  • Define, track and report on service-level indicators (SLIs) for data freshness, completeness and accuracy to ensure SLA commitments for reporting consumers.
  • Create ad-hoc analyses, forecasts and scenario models to support strategic initiatives such as pricing, capacity planning or go-to-market optimization.
  • Develop and maintain technical and business-facing documentation: data dictionaries, lineage diagrams, SOPs and runbooks for support and audits.
  • Support data governance efforts by documenting policies, participating in stewardship committees and enforcing standards for data definitions and usage.
  • Train and mentor business users and junior analysts on reporting best practices, SQL fundamentals and effective use of BI tools.
  • Lead and participate in cross-functional project workstreams to implement new modules, integrations, or process transformations with clear data acceptance criteria.
  • Monitor and triage production incidents related to data pipelines or reporting, coordinating fixes and communicating impact and timelines to stakeholders.
  • Evaluate third-party data products and integration tools, providing recommendations based on cost, scalability and alignment with the company’s architecture.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Provide subject-matter expertise during vendor selection and implementation of business system modules.
  • Assist with regulatory reporting and internal/supported audits by supplying reconciliations and source-to-report mappings.
  • Monitor adoption of analytics products, gather feedback, and prioritize improvements to increase business value.
  • Help scope backlog items, estimate effort for data and reporting tasks, and refine user stories for implementable deliverables.
  • Support data privacy and compliance activities, ensuring PII and regulated data are handled per policy.
  • Create templates and reusable components to accelerate delivery of standard reports and reduce technical debt.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL proficiency: complex joins, window functions, CTEs, performance tuning and query optimization.
  • BI and visualization tools: Tableau, Power BI, Looker, Qlik or comparable platforms; ability to design intuitive dashboards and visual analytics.
  • Data modeling: star/snowflake schemas, dimensional modeling and designing for analytical workloads.
  • ETL/ELT development: experience with dbt, Informatica, Talend, Fivetran, Stitch, or custom Python/SQL pipelines.
  • Data warehouse/cloud platforms: Snowflake, Redshift, BigQuery, Azure Synapse or similar.
  • Scripting and automation: Python or R for data manipulation, automation and lightweight data engineering tasks.
  • Business systems experience: hands-on with ERP (SAP, Oracle, NetSuite), CRM (Salesforce), or order management systems.
  • API integrations and middleware: working knowledge of REST APIs, webhooks, MuleSoft or similar integration platforms.
  • Data governance and quality tools: implementation of validation frameworks, lineage, metadata management and stewardship processes.
  • Reporting automation and scheduling: experience with report distribution, versioning and alerting frameworks.
  • Familiarity with Agile development practices, backlog management tools (Jira, Trello) and collaborative documentation (Confluence).
  • Version control and collaboration: basic Git workflows for managing analytics code and dbt models.
  • Security and access control: understanding of RBAC, row-level security, and PII encryption/masking practices.

Soft Skills

  • Strong stakeholder management and ability to translate between technical and non-technical audiences.
  • Excellent written and verbal communication for requirements, technical documentation and executive reporting.
  • Analytical problem-solving with attention to detail and a bias for data-driven decisions.
  • Project management and prioritization skills to balance competing requests and deliver on time.
  • Collaboration and team orientation across business, product and engineering partners.
  • Curiosity and continuous learning mindset to adopt new analytics tools and best practices.
  • Change management aptitude: shepherding process improvements and driving adoption across user communities.
  • Time management and autonomy: capable of owning deliverables end-to-end with minimal supervision.
  • Critical thinking to assess data reliability and recommend remediation or design alternatives.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s degree in Computer Science, Data Science, Information Systems, Business Analytics, Finance, Economics, or related field.

Preferred Education:

  • Master’s degree in Data Science, Business Analytics, Information Systems or an MBA with strong analytics focus; relevant industry certifications (e.g., Tableau, Power BI, Snowflake, Salesforce Administrator) are a plus.

Relevant Fields of Study:

  • Data Science / Analytics
  • Information Systems / Computer Science
  • Business Administration / Finance / Economics
  • Operations Research / Industrial Engineering

Experience Requirements

Typical Experience Range:

  • 3–7 years of professional experience in analytics, business systems, BI, or data roles; may vary by company size and complexity.

Preferred:

  • 5+ years working with business systems (ERP/CRM) and enterprise-class data warehouses, demonstrated history of building production BI solutions, and experience collaborating with engineering teams on data pipelines.
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