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Medium — senior specialized data lead at a known services firm in a metro market.
High — role requires deep Big Data, Spark, Hadoop, and cloud data engineering expertise.
High — explicit 8-14 years and deep Big Data, Spark, and cloud Databricks requirements.
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Lead design and implementation of enterprise-wide Big Data solutions using distributed computing technologies.
Develop, optimize, and maintain scalable data processing pipelines with Apache Spark, including stream-processing with Apache Storm or Spark-Streaming.
Manage and mentor a team of engineers, driving Agile practices to ensure high-quality, efficient delivery of data engineering projects.
8-14 years of experience in Big Data technologies and related fields.
Expertise in Apache Spark, Hadoop v2, MapReduce, HDFS, Sqoop, and messaging systems like Kafka or RabbitMQ.
Proficiency in Python programming and experience with Big Data querying tools (Hive, Impala) plus NoSQL databases (HBase, Cassandra, MongoDB).
Experience with native cloud data services on Azure or AWS using Databricks and proven capability to lead a technical team.
Experienced in leading complex Big Data solution design and implementation in large or multinational organizations.
Strong technical leader familiar with Agile methodologies and cloud-native data engineering best practices.
Technical proficiency spanning distributed computing, ETL frameworks, stream processing, and cross-technology data integration in cloud and on-premise environments.