About the Role
This is a fully hands-on, individual contributor Data Engineering role embedded within a data infrastructure initiative at a large enterprise. You will own the full lifecycle of AWS-based data pipelines, from ingesting and streaming source data through to delivering clean, consumer-ready datasets for downstream use.
What You'll Do
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Stream and process source data from mainframe and legacy systems, loading it into AWS S3.
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Design and implement ETL/ELT workflows using AWS Glue.
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Perform data reconciliation, validation, and quality checks to ensure data accuracy and reliability.
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Curate and transform data, provisioning clean datasets through AWS Aurora and RDS (PostgreSQL).
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Manage S3 storage including data retention policies, archival strategies, and lifecycle management.
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Automate workflows and manage deployments using GitHub and GitHub Actions-based CI/CD pipelines.
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Leverage AI tools to improve engineering productivity and automate data workflows.
What We're Looking For
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5 or more years of professional Data Engineering experience building and delivering data pipelines, ETL/ELT workflows, or data platform solutions.
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Hands-on production experience with AWS Glue for designing and implementing ETL/ELT workflows.
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Strong working knowledge of AWS cloud services including S3, RDS/Aurora, and Glue.
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Demonstrated experience building and maintaining data streaming pipelines using Kafka.
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Experience implementing data reconciliation, data quality checks, and validation processes.
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Proficiency with GitHub repository management and GitHub Actions for CI/CD automation.
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Experience using AI tools to automate workflows and improve productivity.
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Ability to take ownership of deliverables and drive work to completion with minimal oversight.
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Experience with mainframe data sources or legacy system integration is a plus.
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Familiarity with MongoDB or other NoSQL databases is a plus.
Compensation & Benefits
This role pays $65/hr on a W2 basis.
Location
This role is 100% remote.
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