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PwC brand, mid-level data role, metro location, and broad required skillset increase competition.
Broad data engineering and cloud skills (SQL, Spark, Python, AWS) are highly transferable across industries.
Explicit 5-8 years plus mandatory tech stack (SQL, Spark, AWS, Kafka, Airflow) narrows candidate pool.
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Design, develop, and implement data pipelines and integration solutions focusing on big data technologies and cloud AWS environment.
Transform raw data into actionable insights by leveraging SQL, Spark, and programming languages like Scala and/or Python.
Manage and utilize platforms/tools such as Kafka, Airflow, DBT, Flink, Apache Iceberg, Datadog, and possibly Snowflake and CI/CD frameworks for robust data processing.
5-8 years of work experience in data engineering or related roles.
Bachelor's degree in Engineering (B.Tech, M.Tech, M.E, MCA, or B.E) is mandatory.
Strong skills in Big Data, AWS (including S3, Glue, EMR, Aurora PostGres, Lambda, Kinesis), SQL, Python/Scala, Spark, Kafka, Airflow, DBT, Flink, Apache Iceberg, and Datadog.
Experience with Snowflake and CI/CD framework implementation with code repositories like Github is a strong plus.
Experienced professional comfortable operating in a cloud AWS big data environment with expertise in building and optimizing data pipelines and infrastructures.
Skilled in multiple programming languages (Scala and/or Python) and familiar with modern data engineering tools and frameworks such as Kafka, Airflow, DBT, and Flink.
Capable of implementing scalable data solutions with end-to-end knowledge of data integration, transformation, and monitoring within data analytics advisory contexts.