Big Data - Senior Engineer

Job Summary

Big Data - Senior Engineer

Key Responsibilities

• Design scalable Big Data solutions using Apache Spark, Hadoop, Azure Databricks, and modern data platform technologies.
• Define data processing architecture, transformation strategies, and engineering standards aligned with business objectives.
• Lead development of distributed data processing pipelines using Spark (Scala or PySpark) and Hadoop ecosystem technologies.
• Design and optimize SQL and Hive-based data processing solutions to improve performance and scalability.
• Architect and optimize Azure Databricks solutions supporting large-scale data engineering and analytics workloads.
• Design and implement data Lakehouse solutions leveraging Apache Hudi or Apache Iceberg.
• Establish data ingestion, transformation, validation, and reconciliation frameworks to improve data reliability.
• Drive performance tuning initiatives across Spark jobs, Databricks workloads, Hive queries, and Hadoop processing environments.
• Review data engineering solutions to ensure adherence to architecture, performance, and engineering standards.
• Troubleshoot complex data processing, performance, and platform issues through detailed root cause analysis.
• Mentor team members on Spark, Hadoop, Databricks, Hudi/Iceberg, SQL optimization, and Big Data engineering best practices.
• Collaborate with various teams and stakeholders to support end-to-end data platform delivery.
• Drive continuous improvement initiatives focused on scalability, performance, reliability, and operational efficiency.

Behavioral Competencies

• Demonstrates strong ownership while driving Big Data Engineering excellence.
• Collaborate effectively with various teams and business stakeholders to ensure smooth delivery.
• Promotes quality-focused engineering through proactive validation, optimization, and continuous improvement.
• Applies strong analytical thinking to evaluate complex data engineering and platform challenges.
• Demonstrate adaptability while managing evolving technologies, data ecosystems, and business requirements.
• Communicates effectively regarding delivery status, risks, dependencies, and improvement opportunities.
• Maintains high attention to detail across data architecture, processing design, testing, and implementation activities.
• Encourages continuous improvement in data engineering practices and platform operations.
• Supports knowledge sharing and mentoring to strengthen team capabilities.
• Balances scalability, performance, reliability, and business priorities while driving delivery excellence.

Mandatory Competencies

• Programming Language - Scala - Scala
• Big Data - Big Data - Hive
• Big Data - Big Data - Cloudera
• Cloud - Cloud - Apache Hudi
• Big Data - Big Data - Hadoop
• Big Data - Big Data - Sqoop
• Big Data - Big Data - PySpark

Requirements

• Experience: 6-7 Years
• Location: Noida, Gurugram, Pune
• Mandatory Skills: Data Ingestion Tools (Sqoop), Hadoop (HDFS + YARN), Azure Databricks, Hadoop Ecosystem Fundamentals (HBase + Impala), Hive, Scala, Apache Hudi
• Additional Skills: PySpark