EY - GDS Consulting - AI And DATA - Data Engineer-Staff

Staff Data Engineer

The Opportunity:

Join our dynamic team as a Senior Data Engineer, where you will play a critical role in designing, building, and optimizing scalable data solutions that drive business insights and innovation. You will work with cutting-edge technologies in a collaborative Agile environment, contributing to the full data lifecycle from ingestion to transformation and deployment.

Key Responsibilities:

• Design, develop, and manage robust CI/CD pipelines to streamline the product development lifecycle and ensure seamless deployment of data solutions.
• Perform data mapping, and integration between diverse source and target systems to support analytics and business intelligence needs.
• Build and optimize Data & AI solutions, including data engineering pipelines, data modeling, and performance tuning, with 3-5 years of relevant experience.
• Develop high-quality, production-level code primarily in Python, PySpark, and SQL, ensuring maintainability and scalability.
• Implement enterprise-scale production deployments, applying DevOps best practices, with hands-on experience in Git version control.
• Collaborate effectively within Agile teams, contributing to sprint planning, reviews, and continuous improvement.
• Apply strong knowledge of Data Governance and Data Quality standards to maintain data integrity and compliance.

Technical Skills & Experience:

• Extensive experience in building data ingestion frameworks and scalable data pipelines.
• Proven expertise in AWS cloud services including S3, EC2, Glue, Lambda, and Secrets Manager for secure and efficient data processing.
• Hands-on experience with Databricks for unified analytics and collaborative data engineering.
• Proficient in DBT (Data Build Tool) for data transformation and modeling within modern data stacks.
• Skilled in orchestrating workflows using Apache Airflow to automate complex data pipelines.
• Strong programming skills in Python, PySpark, Spark, and Scala for big data processing.
• Experience with software version control and CI/CD tools such as Git, Jenkins, and Apache Subversion.
• AWS / Databricks certifications or equivalent professional technical certifications are highly desirable.
• Familiarity with cloud and enterprise integration technologies.
• Demonstrated ability to write efficient Spark jobs and optimize performance in large-scale environments.
• Excellent analytical skills with deep knowledge of SQL and data querying.
• Minimum 3 years of experience working in very large data warehousing environments.
• Strong communication skills, both written and verbal, to effectively collaborate with cross-functional teams.
• At least 3 years of experience with data warehouse architectures, ETL/ELT processes, and reporting/analytics tools.
• Experience in Python and/or Java development in data engineering contexts.
• Familiarity with Big Data ecosystems including EMR, Hadoop, Databricks, Hive, and Pyspark.