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Tier-1 brand, metro location, and a popular data engineering title increase applicant competition.
Core data engineering skills are broadly transferable across industries.
Extensive mandatory tech stack and leadership expectations make filters stringent.
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Lead the design and development of scalable batch and real-time data pipelines integrating Data Lakes and Data Warehouses using cloud-native technologies.
Own cloud platform operation and optimization across AWS, Azure, or GCP with focus on performance, cost efficiency, security, and disaster recovery.
Provide technical leadership including mentoring, code and architecture reviews, and driving engineering excellence across globally distributed teams.
Proficiency in Java (JDK 8+) with strong OO programming and design skills; experience with Python or Go is a plus.
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and cloud-native data services such as S3, ADLS, GCS, Databricks, EMR, BigQuery, or Redshift.
Strong expertise in Apache Spark (Core, SQL, Structured Streaming) and Kafka; experience with real-time processing frameworks like Apache Flink preferred.
Bachelor’s degree in Computer Science, IT, Engineering, or related field. Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building large-scale, cloud-native data platforms combining batch and streaming data pipelines with strong emphasis on performance and scalability.
Proven technical leader comfortable collaborating with cross-functional, globally distributed teams and mentoring engineers.
Strong in end-to-end data engineering including ETL/ELT design, infrastructure as code, CI/CD, testing, and secure data access controls aligned with enterprise standards.