Senior Data Engineer

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Delhivery

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Job Summary


Job Type
-

Seniority

Years of Experience
Information not provided

Tech Stacks
Hadoop Scala SQL kafka Spark Hive Apache HBase HDFS RedShift Spark Streaming Parcel

Job Description

About Delhivery:

Delhivery is India’s leading fulfillment platform for digital commerce. With a vast logistics network spanning 18,000+ pin codes and over 2,500 cities, Delhivery provides a comprehensive suite of services including express parcel transportation, freight solutions, reverse logistics, cross-border commerce, warehousing, and cutting-edge technology services. Since 2011, we’ve fulfilled over 550 million transactions and empowered 10,000+ businesses, from startups to large enterprises.



Vision : To become the operating system for commerce in India by combining world-class infrastructure, robust logistics operations, and technology excellence.


About the Role: Data Engineer / Sr. Data Engineer:


We're looking for a Data Engineer/ Sr. Data Engineer who can design, optimize, and own our high-throughput data infrastructure. Are you a passionate Kafka and Kafka Connect ecosystem data engineer looking for an exciting opportunity to work on cutting-edge big data projects? Look no further! Delhivery is seeking a talented and motivated Streaming Data Engineering Expert to join our dynamic team.


Are you a passionate Spark and Scala developer looking for an exciting opportunity to work on cutting-edge big data projects? Look no further! Delhivery is seeking a talented and motivated Spark & Scala Expert to join our dynamic team.


Responsibilites:

  • Develop and optimize Spark applications to process large-scale data efficiently.
  • Collaborate with cross-functional teams to design and implement data-driven solutions.
  • Troubleshoot and resolve performance issues in Spark jobs.
  • Stay up-to-date with the latest trends and advancements in Spark and Scala technologies.


Requirements:

  • Proficient in Redshift, data pipelines, Kafka, Real time streaming,etc.
  • Strong experience with Apache Spark, Spark Streaming, and Spark SQL.
  • Solid understanding of distributed systems, Databases, System design, and big data processing frameworks.
  • Familiarity with Hadoop ecosystem components (HDFS, Hive, HBase) is a plus.

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