Senior Data Engineer

Role overview

Work arrangement

• Candidates must be able to accommodate a hybrid schedule and be close to the Charlotte, North Carolina office.

Responsibilities

• Set the technical vision for the Azure and Snowflake data architecture and guide use of Snowflake, dbt, Fivetran, Azure Data Lake, Synapse, and Data Factory.
• Design and implement scalable, resilient ELT/ETL pipelines for mission-critical financial workloads.
• Write, optimize, and review complex Python and SQL; establish architecture, coding, testing, documentation, and code-review standards.
• Integrate data solutions with CI/CD and DevOps, including automated testing, repeatable deployments, and operational readiness.
• Own DataOps reliability: data quality, observability, freshness, volume, lineage, cataloging, SLAs/SLOs, incident response, and post-mortems.
• Implement governance and security controls for financial data, including PII masking, tokenization, role-based access, lineage, and auditing.
• Optimize Snowflake and Azure costs; advise on build-versus-buy decisions for data technologies.
• Partner on AI/ML training, inference, MLOps, feature sets, and GenAI initiatives using Snowflake Cortex or open-source frameworks.
• Mentor data engineers, lead technical design sessions, and explain trade-offs to executive stakeholders.

Experience and qualifications

• 10 or more years of progressive Data Engineering or Software Engineering experience, with significant cloud data platform experience.
• Expert Python and advanced SQL skills, including production pipeline development, optimization, and troubleshooting.
• Deep Snowflake architecture and performance tuning experience and experience with scalable Microsoft Azure data platforms.
• Knowledge of modern data engineering tools and patterns, data modeling, DataOps, reliability, security, and compliance.
• Working knowledge of AI/ML data architecture, MLOps, feature engineering, and generative AI.
• Experience leading technical design, setting engineering standards, mentoring engineers, and supporting large-scale business-critical data systems.
• Financial Services or Asset Management experience is required.
• A related bachelor’s degree is preferred; equivalent practical experience may be considered. Relevant certifications are a plus.

Tools and technical areas

• Tools include Snowflake, dbt, Fivetran, Azure Data Lake, Azure Synapse, Azure Data Factory, Azure DevOps, and GitHub Actions.
• Technical areas include Python, SQL, ELT/ETL, Lakehouse patterns, dimensional modeling, Data Vault, DataOps, CI/CD, and MLOps.
• Security and governance areas include PII masking, tokenization, role-based access control, data lineage, cataloging, and auditing.

Compensation

• Base salary range is $170,000–$190,000 per year, with eligibility for additional variable incentive compensation.

Benefits

• Benefits include paid time off and sick time, 401(k) employer match, medical, dental and vision coverage, HSA contribution for eligible plans, volunteer time, career development, recognition, and fitness reimbursement.