EY - GDS Consulting - AI And DATA - AWS Data Engineer - Senior

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AWS Data Engineer- Senior
We are seeking a highly skilled and motivated AWS Data Engineer with 3-7 years of experience in AWS Glue, AWS Redshift, S3, and Python to join our dynamic team. As a Data Engineer, you will be responsible for designing, developing, and optimizing data pipelines and solutions that support business intelligence, analytics, and large-scale data processing. You will work closely with data scientists, analysts, and other engineering teams to ensure seamless data flow across our systems.

Key Responsibilities:

• Design and Develop ETL Pipelines: Leverage AWS Glue to design and implement scalable ETL (Extract, Transform, Load) processes that move and transform data from various sources into AWS Redshift or other storage systems.
• Engineering governed, batch and near real time data pipelines using AWS native technologies like DirectConnect, S3, Lambda functions, Glue, Kinesis and CloudTrail or equivalent
• Designing and implementing serverless data engineering workloads using AWS ecosystem , taking inputs from S3, RDS, and other cloud based sources (ex: SaaS data) , applying business transformations using distributed compute (ex : EMR, Glue, Spark, etc. ) and persisting insights in the target store (ex: S3, Redshift, DynamoDB)
• Maintain, optimize, and scale AWS Redshift clusters to ensure efficient data storage, retrieval, and query performance.
• Utilize Amazon S3 to store raw data, manage large datasets, and integrate with other AWS services to ensure secure, scalable, and cost-effective data solutions.
• Create and manage AWS Glue crawlers and jobs to automate data cataloging and ingestion processes across various structured and unstructured data sources.
• Use Python (and PySpark within Glue) to write scripts for data transformation, integration, and automation tasks, ensuring clean, efficient, and reusable code.
• Ensure data accuracy and integrity by implementing data validation, cleansing, and error-handling processes in ETL pipelines.
• Optimize AWS Glue jobs, Redshift queries, and data flows to ensure optimal performance and reduce processing times and costs.
• Enable data consumption from reporting and analytics business applications using AWS services (ex: QuickSight, Sagemaker, JDBC / ODBC connectivity, etc.)
• Experience in identify, define and design logical data model, required entities, relationships, data constraints and dependencies focused on enabling reporting and analytics business use cases
• Work closely with data scientists, analysts, and stakeholders to understand data requirements and provide solutions that enable data-driven decision-making.
• Monitoring and Troubleshooting: Develop and implement monitoring strategies to ensure data pipelines are running smoothly. Quickly troubleshoot and resolve any data-related issues or failures.

Required Skills and Qualifications:

• 3-7 years of experience in data engineering or a similar role, with a focus on AWS technologies.
• Academic background in Computer science
• Strong background in programming- PySpark, SQL, Stored Procedures, Python
• Strong experience with AWS Glue building ETL pipelines, managing crawlers, and working with Glue data catalogue.
• Proficiency in AWS Redshift designing and managing Redshift clusters, writing complex SQL queries, and optimizing query performance.
• Hands-on experience with Amazon S3 data storage, data lifecycle policies, and integration with other AWS services.
• Solid programming skills in Python especially for data manipulation (using libraries like pandas) and automation of ETL jobs.
• Experience with PySpark within AWS Glue for large-scale data transformations.
• Proficiency in writing and optimizing SQL queries for data manipulation and reporting.
• Familiarity with data warehouse concepts: star schemas, partitioning, indexing, and data normalization.
• Strong problem-solving skills and attention to detail.
• Experience with version control systems like SVN, Git.
• Experience with Data Streaming Technologies like AWS Kinesis and Kafka Implementation on AWS

Good To have :

• Knowledge of AWS IAM for managing secure access to data resources.
• Familiarity with DevOps practices and automation tools like Terraform or CloudFormation.
• Experience with data visualization tools like QuickSight or integrating Redshift data with BI tools (Tableau, PowerBI, etc.).
• AWS certifications such as AWS Certified Data Analytics - Specialty or AWS Certified Solutions Architect are a plus.

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