Data Analytics Lead

Key Responsibilities

Business Problem Translation & Value Delivery

• Partner directly with business leaders to understand key challenges, pain points, and opportunities

• Translate ambiguous business problems into clear, actionable data and analytics solutions

• Identify high-value use cases that drive measurable outcomes (cost reduction, revenue growth, operational efficiency)

• Ensure all data initiatives are aligned to business KPIs and decision-making needs

• Act as a thought partner to the business on how to leverage data strategically

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MVP-Driven Delivery & Execution

• Lead rapid delivery of Minimum Viable Products (MVPs) to validate business value quickly

• Emphasize speed to insight over perfection—iterate based on feedback

• Break down complex initiatives into incremental, deliverable components

• Remove bottlenecks and drive execution across teams

• Balance short-term wins with long-term scalability

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Data Solution Development (Hands-On Leadership)

• Design and develop analytical datasets, models, and solutions using:

o SQL (advanced)

o Python / PySpark

o Data visualization tools (Power BI)

• Build prototypes, dashboards, and analytical models to solve real business problems

• Guide and review technical work across data engineers and analysts

• Ensure solutions are both technically sound and business-relevant

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Platform Utilization & Oversight

• Leverage modern data platform capabilities:

o Azure Data Factory (ADF)

o Databricks (Spark, Delta Lake)

o Azure Data Lake (Bronze/Silver/Gold)

o Power BI

• Ensure solutions are aligned with architectural standards while maintaining delivery speed

• Support coexistence and transition from legacy (SSIS/SQL Server) to modern platforms

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Change Management & Adoption

• Lead organizational change related to data and analytics adoption

• Drive user adoption of dashboards, reports, and data products

• Communicate value and impact of data solutions in business terms

• Train and enable business users to leverage data effectively

• Influence stakeholders to adopt new ways of working with data

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Team Leadership & Coordination

• Lead cross-functional delivery across data engineers, analysts, and BI developers

• Foster a culture of accountability, speed, and business focus

• Mentor team members on both technical and business-facing skills

• Align team efforts with business priorities and outcomes

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Required Qualifications

• 6–10 years of experience in data, analytics, or data engineering roles

• Proven experience translating business problems into data and analytics solutions

• Expert-level SQL skills (complex transformations, performance optimization)

• Strong experience with Python and/or PySpark

• Hands-on experience with data visualization tools (Power BI strongly preferred)

• Strong Power BI (PBI) skills, including advanced DAX

• Experience working with Azure data platforms (ADF, Databricks, ADLS)

• Strong understanding of data modeling and analytics solution design

• Demonstrated experience delivering MVPs and iterative solutions

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Preferred Qualifications

• Experience in ERP-driven or manufacturing environments

• Experience leading data platform migrations (SSIS → Azure/Databricks)

• Familiarity with Medallion architecture (Bronze/Silver/Gold)

• Experience with Agile, product-based, or iterative delivery models

• Exposure to DataOps practices and CI/CD

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Technical Skills

• Advanced SQL (T-SQL / Spark SQL)

• Python / PySpark

• Databricks (Spark, Delta Lake)

• Azure Data Factory (ADF)

• Azure Data Lake Storage (ADLS)

• Power BI (data modeling, visualization, performance tuning)

• Data modeling (dimensional and analytical)

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Leadership & Business Skills

• Strong business acumen with the ability to connect data to outcomes

• Exceptional ability to translate complex business needs into practical data solutions

• Bias for action—focus on delivering value quickly rather than over-engineering

• Strong change management and stakeholder influence skills

• Excellent communication and storytelling with data

• Ability to drive alignment across business and technical teams

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What Success Looks Like

• Rapid delivery of MVP data solutions that demonstrate clear business value

• High adoption of analytics solutions across business teams

• Strong alignment between data initiatives and business priorities

• Measurable impact from data (efficiency gains, improved decision-making, revenue/cost outcomes)

• Effective transition from legacy to modern data platforms without slowing delivery

• A culture of speed, pragmatism, and continuous improvement

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