Key Responsibilities

Data Analytics &

- Business Intelligence

- Design, develop, and maintain enterprise-scale analytics and reporting solutions.

- Analyze large and complex datasets to generate actionable business insights.

- Develop dashboards, reports, KPIs, and performance monitoring frameworks.

- Translate business requirements into scalable analytical solutions.

Big Data Management

- Manage and support Hadoop ecosystem components including HDFS, Hive, HBase, Sqoop, Spark, Impala, and related technologies.

- Develop and optimize big data processing workflows for structured and unstructured datasets.

- Ensure data quality, integrity, security, and governance across platforms.

- Monitor cluster performance and recommend optimization strategies.

Data Engineering &

- ETL

- Design, build, and maintain ETL/ELT pipelines for large-scale data processing.

- Integrate data from multiple internal and external sources.

- Support data warehousing initiatives and enterprise data models.

- Troubleshoot and resolve data processing and performance issues.

Database Development &

- Optimization

- Develop complex SQL queries, stored procedures, and data transformation scripts.

- Optimize query performance and database workloads.

- Conduct data validation, reconciliation, and quality assessments.

- Support performance tuning activities across data platforms.

Stakeholder Management

- Collaborate with business users, functional teams, and technology stakeholders to gather requirements.

- Present analytical findings and recommendations to project stakeholders.

- Support project planning, estimation, and delivery activities.

- Contribute to proposal development, solutioning, and client presentations where required.

Project Delivery

- Work within Agile/Scrum delivery frameworks.





- Participate in sprint planning, backlog grooming, and release management activities.

- Ensure timely delivery of project milestones while maintaining quality standards.

- Prepare technical documentation, data dictionaries, and knowledge transfer artifacts.

Required Experience

- 6-10 years of experience in Data Analytics, Business Intelligence, Data Engineering, or Big Data environments.

- Hands-on experience with Hadoop clusters and ecosystem tools such as Hive, Spark, HBase, Sqoop, Impala, and Kafka.

- Robust expertise in SQL development, query optimization, and database performance tuning.

- Experience handling large-scale datasets ranging from millions to billions of records.

- Proven experience in designing and managing ETL processes and data warehousing solutions.

- Working knowledge of Agile project delivery methodologies.

- Experience in Banking, Financial Services, Insurance (BFSI), Telecom, Retail, Government, Healthcare, or similar domains will be preferred.

Technical Skills

Essential Skills

- SQL, Advanced SQL Programming

- Hadoop Ecosystem

- Apache Spark

- Hive / HBase

- ETL Development

- Data Warehousing

- Data Modeling

- Business Intelligence &
- Reporting

- Performance Optimization

- Data Quality Management

Preferred Skills

- Python / PySpark

- Power BI / Tableau / Qlik

- Azure Data Lake / Azure Synapse

- AWS Big Data Services

- Databricks

- Kafka

- Snowflake

- Airflow

- Machine Learning Fundamentals

Educational Qualifications

- Bachelor's Degree in Computer Science, Information Technology, Engineering, Data Science, Statistics, Mathematics, or a related discipline from a recognized university.

- Master's Degree (MBA, MCA, M.Tech, MSc Analytics/Data Science, or equivalent) will be an added advantage.

📌 Data Analyst (Delhi)
🏢 KPMG Assurance and Consulting Services
📍 Delhi