Data Solutions Analyst – Enterprise Data

Amtex Enterprises Inc.; United States; Remote; USD 85,000 - 125,000; Full time

Job summary

Amtex Enterprises Inc. is seeking a Data Solutions Analyst – Enterprise Data to support the development and adoption of client governed enterprise data products.

The role combines SQL-based data analysis, data validation, documentation, UAT, and Power BI report development, using Gold Layer datasets and enterprise semantic models to create reliable reports and support KPI and master data initiatives. The successful candidate will work with Azure Data Engineering and BI teams, validate solutions,

Qualifications

• Bachelor’s degree in a related field or equivalent practical experience.
• 1–3 years of experience in data analysis, BI reporting, or analytics.
• SQL skills: joins, aggregations, filtering, data-quality checks.
• Hands-on experience creating Power BI reports and dashboards.
• Familiarity with Power BI semantic models, DIM models, and DAX concepts.
• Exposure to Azure Data Lake, Databricks, Azure Data Factory or similar cloud environments.

Responsibilities

• Gather and document business requirements, KPI definitions, reporting needs, master data rules, and acceptance criteria.
• Write and validate SQL queries to profile data, investigate issues, reconcile results, and support metric logic.
• Build and maintain Power BI reports and dashboards using approved Gold Layer data and governed semantic models.
• Create report measures, visuals, filters, drill paths, and user experiences within established semantic model and KPI standards.
• Validate Power BI report results against certified metrics, source data, and approved business requirements; identify inconsistencies.
• Perform data profiling, reconciliation, QA, regression testing, and UAT support across source systems and enterprise data products.
• Prepare and maintain test cases, reconciliation evidence, source-to-target mappings, report documentation, metric definitions, and business-to-technical mappings.
• Support master data initiatives by identifying duplicates, missing identifiers, hierarchy issues, conflicting values, and data-quality exceptions.
• Test approved master data matching, standardization, hierarchy, crosswalk, and exception-handling rules and document results for review.
• Support controlled self-service reporting by helping users understand available Gold Layer data, certified KPIs, semantic models, and appropriate report-development practices.
• Assist with the preparation of governed datasets, definitions, metadata, and example questions used by Databricks Genie, copilots, and other AI-enabled analytics solutions.
• Test AI-generated analytical responses against certified metrics and approved reports, document inconsistencies, and elevate data or definition issues.
• Collaborate with Data Engineering and Business Intelligence teams to validate implemented solutions while following standards for production support and access administration.
• Contribute to repeatable processes for report development, data validation, documentation, certification support, and issue tracking.

Skills

SQL queries

Power BI

Data governance

Data quality

Communication

Education

Bachelor’s degree

Tools

Power BI

Azure Data Factory

Databricks

Azure Data Lake

Microsoft Fabric

Job description

Job Title: Data Solutions Analyst – Enterprise Data

Location: Remote / Home-Based, US Based

Duration: 6+ Months

Rate: Open- Keep Competitive

Position Summary

We are seeking a Data Solutions Analyst – Enterprise Data to support the development and adoption of client governed enterprise data products. Working under the direction of the Senior Data Solutions Analyst and in partnership with Azure Data Engineering and Business Intelligence teams, this role combines SQL-based data analysis, data validation, documentation, user acceptance testing, and Power BI report development. The successful candidate will use approved Gold Layer datasets and enterprise semantic models to create reliable reports, support certified KPI and master data initiatives, and help validate trusted data for reporting, analytics, and AI-enabled use cases.

Responsibilities

• Support the Senior Data Solutions Analyst in gathering and documenting business requirements, KPI definitions, reporting needs, master data rules, and acceptance criteria.
• Write and validate SQL queries to profile data, investigate issues, reconcile results, and support approved metric logic.
• Build and maintain Power BI reports and dashboards using approved Gold Layer data and governed enterprise semantic models.
• Create report measures, visualizations, filters, drill paths, and user experiences within established semantic model and KPI standards.
• Validate Power BI report results against certified metrics, source data, and approved business requirements; identify and elevate inconsistencies.
• Perform data profiling, reconciliation, root-cause analysis, QA, regression testing, and UAT support across source systems and enterprise data products.
• Prepare and maintain test cases, reconciliation evidence, source-to-target mappings, report documentation, metric definitions, and business-to-technical mappings.
• Support client, product, vendor, consumer, and other master data initiatives by identifying duplicates, missing identifiers, hierarchy issues, conflicting values, and other data-quality exceptions.
• Test approved master data matching, standardization, hierarchy, crosswalk, and exception-handling rules and document results for review by senior team members and business data owners.
• Support controlled self-service reporting by helping users understand available Gold Layer data, certified KPIs, semantic models, and appropriate report-development practices.
• Assist with the preparation of governed datasets, definitions, metadata, and example questions used by Databricks Genie, copilots, and other approved AI-enabled analytics solutions.
• Test AI-generated analytical responses against certified metrics and approved reports, document inconsistencies, and elevate data or definition issues.
• Collaborate with Data Engineering and Business Intelligence teams to validate implemented solutions while following established standards for production support and access administration.
• Contribute to repeatable processes for report development, data validation, documentation, certification support, and issue tracking.

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Analytics, Mathematics, Finance, Business, or a related field, or equivalent practical experience.
• 1 to 3 years of experience in data analysis, Business Intelligence, reporting, analytics, or a related role; relevant internship or project experience may be considered.
• Working SQL skills, including joins, aggregations, filtering, data-quality checks, and troubleshooting queries.
• Hands-on experience creating Power BI reports and dashboards, including visualizations, measures, filters, and drill-through behavior.
• Familiarity with Power BI semantic models, dimensional modeling, facts and dimensions, relationships, and DAX concepts.
• Experience with data profiling, reconciliation, testing, or validation of business reports and metrics.
• Exposure to Azure Data Lake, Databricks, Azure Data Factory, Microsoft Fabric, or similar cloud data and analytics environments preferred.
• Interest in master data, data governance, data quality, and the responsible use of governed enterprise data in AI-assisted analytics.
• Strong attention to detail, curiosity, documentation discipline, and analytical problem-solving skills.
• Clear communication skills and the ability to work effectively with business users, senior analysts, Business Intelligence developers, and engineers.

Success Measures

• Accurate, usable Power BI reports built from approved Gold Layer datasets and enterprise semantic models.
• Reliable SQL analysis, reconciliations, testing, and documentation that reduce rework and support timely delivery.
• Consistent report results aligned with certified KPIs and approved business definitions.
• Master data and data-quality issues identified, documented, and escalated with clear supporting evidence.
• Effective support for UAT, controlled self-service reporting, and validation of approved AI-enabled analytics.
• Demonstrated growth in enterprise data, semantic modeling, governance, and business-facing analytical skills.