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Fintech Senior Data Engineer
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Selby Jennings
Atlanta, GA (In Person)
Full-Time
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Job Description
This position is W‑2 and open to U.S. citizens only. Visa sponsorship or C2C engagements are not available. We are working with a leading asset management firm seeking a Senior Data Engineer with deep, hands‑on expertise in SQL Server and advanced T‑SQL, specifically within financial services or asset management environments. This role is ideal for engineers who live in complex stored procedures, performance tuning, data validation frameworks, and operationally critical SQL workloads. You will play a central role in the model delivery platform (ECOS), owning the design, transformation, validation, auditing, and distribution of investment model data across multiple downstream channels, including AWS S3, FTP, email, web platforms, and internal systems. Accuracy, traceability, and reliability are non‑negotiable-this is production‑critical financial data. Core Responsibilities Design, build, and maintain SQL Server-centric data workflows that power model delivery across multiple external and internal destinations. Author and optimize complex T‑SQL (stored procedures, functions, triggers, auditing tables) Performance‑tune SQL Server workloads, including query optimization, indexing strategies, and execution‑plan analysis. Build and maintain Python services and automation tooling that integrate with SQL‑based pipelines. Design and support CI/CD pipelines for data and service deployments. Requirements Bachelor's degree in Computer Science, Engineering, Statistics, Data Science, or a related technical field. 3+ years of professional experience in financial services, asset management, or a closely related regulated domain. 3+ years of deep SQL Server and T‑SQL experience AWS experience, especially S3, Lambda, Step Functions, Fargate, Aurora, and Terraform. 3+ years of Python experience supporting data pipelines, services, or automation. Proven experience designing reliable, scalable data‑delivery systems. Strong understanding of data‑engineering fundamentals: modeling, pipeline design, validation frameworks, lineage, and observability. Hands‑on experience with CI/CD and modern DevOps practices. Experience working with both structured and unstructured data. Preferred Qualifications Prior experience supporting investment models, risk models, or quantitative research workflows. Professional certifications such as CFA, CPA, CIPM, CAIA, or FRM.