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Remote, popular Data Engineer title, metro location, and broad skillset requirements increase candidate competition.
Core data engineering skills (SQL, Python, ETL, AWS) are readily transferable across industries, lowering background sensitivity.
Many mandatory technical and operational requirements (SQL, Python, AWS, production support, CI/CD) imply strict shortlisting.
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Develop, maintain, and support enterprise-scale data pipelines that ingest, transform, and manage multi-source data for Financial Crime Risk and Legal/Corporate Tech platforms.
Ensure operational stability and reliability of production data systems, including participating in incident management and on-call rotations.
Collaborate with cross-functional teams to deliver scalable, resilient, and high-quality data engineering solutions aligned with evolving enterprise frameworks and standards.
Strong hands-on experience developing enterprise-scale data engineering solutions, including advanced SQL and Python skills.
Experience with AWS cloud services such as S3, RDS, EKS, EC2, Lambda, and IAM for cloud-based data platforms.
Experience implementing data quality controls, validation frameworks, reconciliation processes, and operational support including production troubleshooting.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in supporting and modernizing complex data pipelines with an engineering mindset focused on operational excellence and platform resilience.
Familiar with Agile methodologies, CI/CD pipelines, automated testing, and source control in collaborative delivery environments.
Preferably has domain exposure to Financial Crime Risk technologies or regulated sectors, and skills in distributed data processing or orchestration tools.