EY - GDS Consulting - AI and DATA - Data Engineer- Senior

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

AWS technical lead - Senior

Responsibilities

• Help validate client requirements and architecture proposals for AWS components
• Lead a team of data engineers in designing low level design for various data engineering components – ETL/ data quality and lineage from client requirements using tools like Glue/EMR/Redshift and other third party tools like Snowflake
• Lead development of re-usable frameworks and patterns for ingestion, DQ, audit and other data management tasks
• Help troubleshoot performance and cost challenges in solutions with appropriate fixes
• Support upkeep of DevOps pipelines as required
• Help automation of operations through jobs for alerting/monitoring and remediations

Skills

• 3-5 years of work experience with ETL, Data Modeling, and Data Architecture
• A minimum of 1 projects experience with AWS solutions
• Has experience in any of the following AWS services
• Proficiencies in more than 3 analytical services – Glue, EMR, Athena, Redshift, Kinesis, Lambda, DynamoDB, Sagemaker
• Strong experience in the foundational services – S3/Glacier, Cloud trail/watch
• Exposure to third party services like – Snowflake, Matillion, Kafka, Privicera etc.
• Expert-level skills in writing and optimizing SQL.
• Very strong Python and Spark experience, including optimization techniques.
• Expertise in streaming pipeline using Kinesis/Kafka/Beam/Flink big plus
• Experience with Big Data technologies such as Hadoop/Hive/Spark.
• Solid Linux skills.
• Experience working in very large data warehouses or data lakes.
• Ability performance tune data pipeline (Glue/EMR) and Redshift, knows how to optimize the distribution, partitioning, and MPP of high-level data structures.
• Show efficiency in handling data - tracking data lineage, ensuring data quality, and improving discoverability of data.
• Sound understanding of DevOps on AWS with prior experience using AWS or open source stacks

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