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Tier-1 brand, mid-level general role, metro location and broad skillset required.
Core data engineering skills are transferable, though enterprise finance context adds moderate bias.
Multiple mandatory technologies and explicit 4+ years experience increases filter stringency.
Design, develop, and troubleshoot software components and data integration solutions within the firm's Workforce Technology, Employee Platforms team.
Build scalable and extensible data acquisition and integration pipelines using Big Data technologies and AWS services (EMR, S3, Glue, Lambda, Airflow).
Collaborate with cross-functional and remote teams to deliver secure, stable, and maintainable data products and leverage AI-assisted coding tools for enhanced code quality and delivery.
4+ years of experience in data integration projects using Big Data technologies.
Hands-on experience with AWS Data Warehousing and services including EMR, S3, AWS Glue, Lambda, Apache Airflow and Infrastructure as Code tools like Terraform or CloudFormation.
Strong proficiency in Spark engineering with PySpark/Scala and CICD tools such as Jenkins, Git, Artifactory, Yaml, Maven for cloud deployments.
Work Experience Required: Minimum 4 years in relevant data engineering and integration roles.
Experienced in building and optimizing scalable data pipelines using Spark and big data query tools like Athena, RDS, Databricks SQL Warehouse.
Demonstrated knowledge of Data Warehousing, Data Modeling, Data Lake architectures along with Java open-source API standards.
Familiarity with enterprise AI-assisted development tools and adherence to secure, resilient engineering workflows including responsible AI use.