EY
EY

EY is a global professional services firm offering audit, tax, consulting, and advisory expertise. Its teams help organizations address complex business challenges, strengthen trust in financial reporting and markets, and use data and technology to support transformation.

The firm provides services including corporate finance advisory, transaction strategy, and technology consulting. EY works with clients across areas such as healthcare, legal services, marketing, energy, finance, and government, with sustainability and innovation informing many of its solutions.

Senior Data Operations and Engineering Specialist – Noida Onsite

Senior data engineering role at EY focused on cloud data platforms, scalable pipelines, migration, business intelligence, and dependable operations across AWS and Azure.

Description

  • Build and maintain scalable data pipelines and integration frameworks
  • Administer and optimize cloud data platforms, databases, warehouses, and analytical datasets
  • Drive data migration, modernization, and transformation initiatives
  • Partner with business, analytics, and reporting teams to deliver trusted data assets
  • Maintain data quality, security, performance, availability, and operational resilience
  • Enable business intelligence, reporting, and self-service analytics programs
  • Monitor, troubleshoot, and improve data platform operations and processes

Requirements

  • Practical expertise in SQL, Python, and/or PySpark
  • Experience designing and developing ETL/ELT pipelines, data flows, and integration solutions
  • Hands-on experience with AWS services such as S3, Glue, Redshift, Lambda, and Athena, and/or Azure services including ADF, Databricks, ADLS, and Synapse
  • Strong understanding of data warehousing, data lake, lakehouse, and dimensional modeling architectures, including Star and Snowflake schemas
  • Experience delivering and supporting enterprise data platforms for reporting, analytics, and business intelligence
  • Background in data migration and modernization, including on-premises-to-cloud moves and legacy platform transformation
  • Experience using Spark or Databricks for large-scale data processing
  • Knowledge of batch and real-time data processing frameworks
  • Experience with Airflow, ADF, Step Functions, or comparable pipeline orchestration tools
  • Strong understanding of data quality, validation, performance tuning, and operational support
  • Experience supporting BI platforms such as Power BI, Tableau, or QuickSight
  • Familiarity with source control, CI/CD, and Agile delivery practices
  • Experience with Snowflake, Microsoft Fabric, or comparable modern data platforms
  • Exposure to streaming tools such as Kafka, Event Hub, or Kinesis
  • Knowledge of change data capture, replication, and data synchronization frameworks
  • Experience implementing infrastructure as code with Terraform, CloudFormation, or Bicep
  • Understanding of data governance, metadata management, lineage, and security practices
  • Cloud certification such as AWS Data Engineer, DP-203, DP-600, or an equivalent credential

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