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Tier-1 brand, metro location, and mid-level popular data engineering role drive high applicant competition.
Core big-data and cloud skills are transferable, but tooling specificity creates moderate background sensitivity.
Explicit 5-8 years plus extensive mandatory big-data and cloud techs increases shortlist strictness.
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Design and build robust data infrastructure and systems for efficient processing and analysis using big data and cloud technologies.
Develop and implement data pipelines, integration, and transformation solutions utilizing tools like Spark, Kafka, Airflow, DBT, and Flink.
Leverage AWS cloud services and big data technologies to enable actionable business insights and support client decision-making.
5-8 years of work experience in big data engineering or related roles.
Mandatory skills: Big Data, AWS, SQL, Python/Scala, Spark, S3, Glue, EMR, Aurora PostGres, Lambda, Kinesis, Kafka, Airflow, DBT, Flink, Apache Iceberg, Datadog.
Education: Bachelor's degree in Engineering (B.Tech/B.E) or equivalent; M.Tech/M.E or MCA also mentioned but bachelor is minimum.
Experience with cloud AWS services and big data ecosystem tools as listed; Snowflake experience is a strong plus.
Experienced in designing scalable data engineering solutions using big data frameworks and AWS cloud services.
Proficient in multiple programming languages especially Python and/or Scala with Spark expertise.
Skilled in implementing CI/CD pipelines and working knowledge of modern data orchestration tools like Airflow and Kafka, indicating strong operational engineering competence.