Big Data - Senior Engineer

. 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.

. 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.