Senior Power BI & Analytics Engineer

Job Description Senior Power BI & Analytics Engineer

Designation: Senior Power BI & Analytics Engineer
Experience: 5 to 9 Years
Location: Gurugram
Employment Type: Full-Time
Work Mode: As per company policy

Job Summary

We are looking for an experienced Senior Power BI & Analytics Engineer with 4+ years of hands-on experience in Business Intelligence, Data Analytics, ETL development, and reporting automation.

The ideal candidate must have strong expertise in Power BI and Azure

Data Factory (ADF), along with proficiency in SQL and Python. The candidate should have practical experience in designing and developing interactive dashboards, building scalable ETL pipelines, integrating multiple data sources, optimizing data models, and delivering actionable business insights.

The role requires a strong understanding of BI architecture, data warehousing, cloud-based data integration, and end-to-end analytics solutions. The candidate will work closely with business stakeholders, technical teams, and management to understand reporting requirements and deliver efficient, scalable, and reliable analytics solutions.

Key Responsibilities

1. Power BI Development & Reporting

• Design, develop, and maintain interactive dashboards and reports using Microsoft Power BI.
• Create advanced DAX measures, calculated columns, and KPIs to support business reporting.
• Develop optimized data models using Star Schema and Snowflake Schema.
• Connect Power BI with multiple data sources, including SQL Server, Azure services, and other databases.
• Implement Row-Level Security (RLS), workspace management, and report access controls.
• Manage Power BI Service, scheduled refreshes, gateways, and report deployments.
• Optimize dashboard performance, data loading, and report responsiveness.
• Convert business requirements into meaningful visualizations and actionable insights.

2. Azure Data Factory (ADF) ETL Development

• Design, develop, and maintain ETL/ELT pipelines using Azure Data Factory.
• Build data ingestion workflows to extract data from multiple sources and load it into target systems.
• Work with ADF pipelines, datasets, linked services, triggers, and activities.
• Implement data transformation and orchestration workflows.
• Configure scheduled and event-based pipeline triggers.
• Monitor pipeline execution, troubleshoot failures, and resolve data integration issues.
• Implement incremental data loading and automate data refresh processes.
• Ensure data accuracy, consistency, quality, and reliability across pipelines.
• Optimize pipeline performance and manage large-volume data processing.

3. SQL & Database Management

• Write and optimize complex SQL queries for data extraction, transformation, and analysis.
• Work with SQL Server, SSMS, Apache Impala, and other relational databases.
• Develop Joins, CTEs, Subqueries, Stored Procedures, and Window Functions.
• Perform query tuning and improve database performance.
• Manage data validation, aggregation, and transformation processes.
• Work with large datasets and structured data warehouses.

4. Python & Data Analytics

• Use Python libraries such as Pandas and NumPy for data processing and analysis.
• Develop scripts to automate repetitive reporting and data validation tasks.
• Perform data cleaning, preprocessing, and transformation.
• Support analytical solutions and data quality monitoring.
• Work with Python-based automation and analytics workflows.

5. Data Modelling & Business Intelligence

• Design and implement scalable data models for enterprise reporting.
• Develop Star Schema and Snowflake Schema-based data structures.
• Integrate data from multiple sources into centralized reporting environments.
• Establish KPI frameworks and reporting standards.
• Identify data inconsistencies and implement data quality checks.
• Collaborate with stakeholders to understand business requirements and translate them into technical solutions.

6. Cloud & Big Data Technologies

• Work with Azure cloud-based analytics and data engineering platforms.
• Utilize Azure Synapse Analytics and Azure Databricks for data processing and analytics.
• Work with Apache Spark, PySpark, Hadoop, and ADLS.
• Support large-scale data transformation and reporting requirements.
• Contribute to end-to-end cloud-based BI and analytics solutions.

Mandatory Technical Skills

1. Microsoft Power BI: Strong hands-on experience in dashboard development, DAX, Power BI Service, data modelling, and report optimization.
2. Azure Data Factory (ADF): Practical experience in ETL/ELT pipeline development, data integration, pipeline orchestration, and monitoring.
3. SQL: Strong command of complex SQL queries, Joins, CTEs, Stored Procedures, and Query Optimization.
4. Python: Experience with Pandas, NumPy, data manipulation, and automation.
5. ETL/ELT: Hands-on experience in data extraction, transformation, loading, and integration.
6. Data Modelling: Understanding of Star Schema, Snowflake Schema, and data warehousing concepts.

Good to Have Skills

• Azure Synapse Analytics
• Azure Databricks
• Azure Data Lake Storage (ADLS)
• Tableau
• Apache Spark / PySpark
• Hadoop / Apache Impala
• Power Automate
• Advanced Microsoft Excel
• Machine Learning fundamentals
• Katalon Studio

Required Experience & Qualifications

• 46 years of relevant experience in Data Analytics, Business Intelligence, or Data Engineering.
• Strong practical experience in Power BI and Azure Data Factory.
• Experience in developing end-to-end BI and analytics solutions.
• Knowledge of cloud-based data integration and ETL architecture.
• Experience working with large datasets and multiple data sources.
• Bachelor's degree in Computer Science, Information Technology, Data Science, or a related field preferred.

Preferred Candidate Profile

• Strong analytical and problem-solving abilities.
• Ability to independently manage dashboard development and ETL workflows.
• Good understanding of business reporting and KPI development.
• Strong communication and stakeholder management skills.
• Ability to work collaboratively with cross-functional teams.
• Attention to detail and commitment to delivering accurate and reliable data solutions.