Position: Principal Engineer–Data DevOps Job Location: Noida Job Overview: We are seeking an experienced Principal Engineer (Data DevOps) to lead our Data DevOps team in building, managing, and optimizing high-scale, secure, and reliable big data platforms.
Description & SummaryA career within Data and Analytics services will provide you with the opportunity to help organisations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organisational
Candidate should be able to: Coordinate Development, Integration, and Production deployments. Optimize Spark code, Impala queries, and Hive partitioning strategy for better scalability, reliability, and performance. Build applications using Maven, SBT and integrated with continuous integration servers
Job Title: Data Engineer Experience: 3–6 Years Location: Noida Employment Type: Full-Time Work Mode: 5 days WFO Job Summary We are looking for a skilled Data Engineer with 3–6 years of experience to design, build, and
Candidate should have: • Experience designing, developing, and testing applications using proven or emerging technologies, in a variety of technologies and environments. • Experience in using and tuning relational databases (Azure SQL Datawarehouse and SQL DB,
• Developing the Best practices documentation to the Application Developer partners to educate how to use the Cloudera Services. • install/Configure/Maintain Apache Cloudera tools like Hive, Impala, Sqoop, Flume, Kafka, HBase, SOLR, and File formats like Avro, Parquet.
• Azure Data Factory • Azure Databricks • Python, Scala, PySpark, Spark • HIVE / HIVE LLAP / HBASE / CosmoDb • Azure Active Directory Domain Services • Apache Ranger / Apache Ambari • Azure Key Vault • Expertise in
• Experience with Hadoop and the HDFS Ecosystem • Strong Experience with Apache Spark, Storm, Kafka is a must. • Experience with Python, R, Pig, Hive, Kafka, Knox, Tomcat and Ambari • Experience with MongoDB • A minimum
• Building efficient storage for structured and unstructured data • Transform and aggregate the data using data processor technologies • Developing and deploying distributed computing Big Data applications using Open Source frameworks like Apache Spark, Apex, Flink,