Data Analyst

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.
• Strong 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.