Key Responsibilities
•    Design, develop, and maintain interactive dashboards, reports, and semantic models using Power BI.
•    Create optimized data models, DAX calculations, KPIs, measures, Power Query transformations, and enterprise reporting solutions.
•    Develop and maintain complex SQL queries, stored procedures, and database objects for analytics and reporting.
•    Build and maintain scalable ETL/ELT pipelines using AWS Glue, Amazon EMR, AWS Lambda, or similar AWS data integration services.
•    Develop Python scripts for automation, data processing, validation, and workflow optimization.
•    Integrate data from multiple enterprise systems including SQL Server, Oracle, SAP, ERP, APIs, AWS services, and cloud data warehouses.
•    Work with modern cloud platforms including AWS, Amazon Redshift, Amazon S3, Databricks, and Power BI.
•    Design dimensional data models (Star Schema/Snowflake Schema) and publish curated datasets for enterprise analytics.
•    Optimize Power BI reports through DAX optimization, query tuning, incremental refresh, aggregation, and performance best practices.
•    Implement Row-Level Security (RLS), workspace governance, deployment pipelines, and Power BI Service administration.
•    Collaborate with Data Engineering teams to design scalable data lake, Lakehouse, and Medallion Architecture solutions.
•    Support deployment, release management, CI/CD implementation, and migration across Development, UAT, and Production environments.
•    Maintain technical documentation for dashboards, datasets, pipelines, and operational runbooks.
•    Troubleshoot reporting, performance, data quality, and production issues while ensuring high availability of analytics solutions.
•    Work closely with business stakeholders to gather reporting requirements and translate them into scalable technical solutions.
Primary Skills (Must Have)
Power BI & Analytics
•    Strong hands-on experience developing enterprise Power BI reports, dashboards, and semantic models.
•    Expertise in DAX, Power Query (M), calculated measures, calculated columns, relationships, and data modeling.
•    Experience publishing and managing datasets using Power BI Service.
•    Knowledge of Row-Level Security (RLS), Incremental Refresh, Gateways, Deployment Pipelines, Workspace Management, and report performance optimization.
•    Experience building executive dashboards, KPI scorecards, and self-service BI solutions.

Programming Languages
•    Advanced SQL (essential) for query optimization, stored procedures, and analytics.
•    Strong Python programming for automation, data processing, scripting, and ETL development.

Data Engineering & ETL
•    Hands-on experience building ETL/ELT pipelines using AWS Glue, Amazon EMR, AWS Lambda, Amazon Kinesis, or similar AWS services.
•    Experience working with Amazon S3 as a data lake.
•    Knowledge of data orchestration, monitoring, and workflow automation using AWS services.
•    Understanding of Medallion Architecture, Lakehouse concepts, and modern data engineering best practices.

Data Warehousing & Cloud Platforms
•    Experience with AWS (Redshift, S3, Glue, Athena, EMR, Lambda, IAM, CloudWatch).
•    Experience with Databricks, or equivalent cloud data platforms.
•    Strong understanding of cloud data warehousing, Lakehouse architecture, and dimensional modeling

Data Modeling & Database
•    Strong understanding of Star Schema, Snowflake Schema, and dimensional modeling.
•    Experience working with SQL Server, Oracle, MySQL, DynamoDB or cloud databases.
•    Knowledge of semantic modeling and enterprise reporting architecture.

Engineering Best Practices
•    Experience using Git/version control.
•    Experience implementing CI/CD pipelines   
•    Familiarity with Agile/Scrum methodologies.
•    Strong documentation, troubleshooting, and code review practices.

Data Governance & Security
•    Experience implementing security using AWS IAM, encryption, secrets management, and data governance best practices.
•    Knowledge of data privacy, RBAC and secure data sharing.
Secondary Skills (Good to Have)
•    Exposure to other cloud analytics platforms such as Azure, Snowflake
Qualification
•    Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
•    5–8 years of experience in Power BI, Data Analytics, and Data Engineering.
•    Strong analytical, troubleshooting, communication, and stakeholder management skills.
•    Ability to work effectively with cross-functional business and technical team.
Preferred Certifications
•    Microsoft Power BI Data Analyst Associate
•    AWS Certified Data Engineer – Associate or AWS Certified Solutions Architect – Associate
•    AWS Certified Developer – Associate  
•    Databricks Fundamentals or Associate Certification  

Tools & Technologies
•    Power BI Desktop & Power BI Service
•    Amazon S3
•    AWS Glue
•    Amazon Redshift
•    Amazon Athena
•    Amazon EMR
•    AWS Lambda
•    Amazon Kinesis
•    AWS IAM & CloudWatch
•    SQL Server / Oracle / MySQL / DynamoDB
•    Python
•    Advanced SQL
•    DAX & Power Query
•    Git / GitHub / AWS CodePipeline / Jenkins