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Tier-1 brand, popular Data Engineer title, mid-senior level, and broad required skillset make competition high.
Core data engineering skills transfer across industries but platform-specific and cloud experience require domain fit, so medium sensitivity.
Mandatory 7+ years and extensive required tech stack and cloud/data platform experience increase shortlisting strictness to high.
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Architect, build, and maintain scalable, resilient, and high-performance data pipelines supporting batch and real-time workloads.
Design and optimize data models, distributed data processing solutions, and data platforms using cloud services and modern data technologies for analytics, ML, and BI.
Lead technical initiatives including data architecture, engineering standards, governance, and mentor junior engineers.
Bachelor's degree in Computer Science, IT, Engineering, or related field (or equivalent experience).
7+ years of professional experience in Data Engineering, Data Platform Engineering, or Distributed Data Systems.
Hands-on expertise in Python, Spark/PySpark, advanced SQL, shell scripting, relational (PostgreSQL/MySQL) and NoSQL databases (MongoDB, Cassandra, DynamoDB, or equivalent).
Experience with cloud platforms (preferably AWS services like EMR, Glue, S3, IAM, Lambda, Step Functions, Athena, Redshift) and modern data warehouse platforms (Snowflake, Redshift, BigQuery).
Experienced in designing and operating scalable ETL/ELT pipelines for batch and streaming environments with modern data orchestration and processing tools (e.g., Apache Airflow, Kafka, Flink).
Proven ability to lead technical data engineering projects, influence architecture decisions, and establish engineering best practices.
Familiarity with enterprise-scale data platforms supporting analytics, AI/ML workloads, and implementing data governance and quality frameworks.