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 robust 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

📌 Big Data - Senior Engineer (Noida)
🏢 IRIS SOFTWARE
📍 Noida