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Mid-level Big Data role in metros with common Spark/Databricks requirements increases candidate competition.
Core Big Data engineering skills are transferable, though platform-specific Databricks/Azure experience increases domain bias.
Explicit 5-8 years and many mandatory Big Data technologies narrow the candidate pool significantly.
Design and implement scalable Big Data solutions using Apache Spark, Hadoop, Azure Databricks, and Lakehouse technologies.
Lead development and optimization of distributed data processing pipelines and SQL/Hive-based solutions to enhance performance and scalability.
Establish frameworks for data ingestion, transformation, validation, and drive continuous improvement initiatives focusing on scalability, performance, and reliability.
5-8 years of relevant work experience in Big Data engineering.
Proficiency with Azure Databricks, Hadoop (HDFS + YARN), Hadoop ecosystem components (HBase, Impala), Scala, SQL, Cloudera, and Apache Iceberg.
Location: Noida, Gurugram, Pune, India.
Work Experience Required: 5-8 years.
Demonstrated ability to lead Big Data solution architecture, focusing on performance tuning and adherence to engineering standards.
Experience mentoring and collaborating with diverse teams to deliver complex data platform projects.
Strong expertise in modern Big Data platforms and distributed data processing, particularly within cloud and Azure Databricks environments.