Live opening · Posted 6 days ago
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We are looking for an experienced Data and Analytics Engineer to join our team and empower data engineers and analysts. Your role will be crucial in demonstrating the practical value of data-driven decision-making. You will work closely with various stakeholders to understand their data needs, guide them through best practices in data engineering, and deliver high-performing reports and visualizations. This position offers an exciting opportunity to contribute to our data-centric culture and drive business insights.
5-10 years of experience as a Data Engineer and Analyst with a strong engineering mindset.
Proficiency in Azure Data Platform technologies, including Microsoft Fabric, Spark, Python, Power BI, and SQL.
Strong analytical skills and experience with Power BI reporting.
Ability to translate business requirements into data and technical design artifacts.
Excellent programming skills in Python and SQL.
Experience in developing executive-level reporting or dashboards.
Strong documentation skills for creating enablement content and video recordings.
Effective communication skills for community engagement, mentoring, and onboarding.
Understanding of data warehousing, data modeling, and data visualization.
Familiarity with Agile delivery, data governance frameworks, and dimensional modeling.
Data Sourcing and Lakehouse Engineering: Build and maintain data layers in Microsoft Fabric and Databricks, optimizing performance and cost efficiency.
Business Logic & Data Product Definition: Translate business requirements into analytical specifications, define KPIs, and collaborate with Data Engineers for accurate transformations.
Semantic Modeling & Analytics Layer Ownership: Design Fabric semantic models, implement star schemas for Power BI, and ensure metric consistency across dashboards.
Power BI & Insight Delivery: Conduct exploratory data analysis, apply statistical models, and design interactive Power BI dashboards, optimizing performance and security.
Reusable Scripts & CI/CD: Develop Python scripts for automation, integrate Power BI with external systems, and automate recurring reports with version control.
Self-Service Analytics Enablement: Define playbooks, design intuitive semantic models, and support the creation of reusable datasets and templates with documentation.
Conversational & AI-Driven Analytics: Define playbooks for AI agents, ensure semantic models support natural language querying, and validate AI-driven outputs.
Legacy BI Transition Support: Share migration best practices for SSRS, SSAS, and SQL-based environments, and contribute to the decommissioning of legacy artifacts.
Continuous Improvement: Stay updated on industry trends, suggest process improvements, and collaborate with the COE community to drive analytics context.
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