data engineer (wealth management) Salary in New York (2026): Complete Guide

A data engineer (wealth management) in New York typically earns $125,000 to $260,000 base salary in 2026, with total compensation often landing between $150,000 and $350,000+ once bonus is included. If you’re at a top-tier bank, hedge fund, or asset manager with strong Python, Spark, and cloud/data platform experience, the upper end is realistic.

Salary by Experience

New York pays a premium because wealth management sits next to other high-paying financial sectors like hedge funds, private equity, and investment banking. That competition pushes compensation up for engineers who can handle regulated data pipelines, client reporting systems, and portfolio analytics at scale.

What Affects Your Salary

• •Asset class exposure matters. If you’ve worked with portfolio accounting, trade data, performance attribution, or client reporting data models, you’ll usually command more than a generic ETL engineer.
• •Cloud and platform depth raises your ceiling. Experience with AWS, Snowflake, Databricks, Airflow, Kafka, dbt, and CI/CD for data pipelines can move you into senior-band pay faster.
• •Regulatory and controls knowledge pays. Firms value engineers who understand auditability, lineage, data quality controls, PII handling, SOC2-style environments, and model risk governance.
• •Firm type changes the range. A large wirehouse or private bank often pays below a hedge fund or multi-family office platform. In New York specifically, buy-side firms tend to pay the strongest cash compensation.
• •Remote vs onsite affects negotiation power. Fully onsite roles in Manhattan sometimes come with a higher base or bonus to offset commute expectations. Fully remote roles outside New York may still benchmark against NYC pay if the team is headquartered there.

How to Negotiate

• •Anchor on business-critical outcomes. Don’t just say you build pipelines. Talk about reducing T+1 reporting delays, improving reconciliation accuracy, lowering failed jobs in production, or supporting advisor/client reporting SLAs.
• •Price in your financial domain experience. If you’ve handled market data normalization, holdings reconciliation, performance reporting, or reference data governance before — say it clearly. Wealth management teams pay more for engineers who already know the workflow.
• •Negotiate total comp separately from base. In New York finance roles that means base salary plus bonus plus deferred comp if applicable. A lower base can still be a good offer if the bonus target is strong and predictable.
• •Use competing offers carefully. Banks and asset managers respond to market signals from nearby firms. If you have an offer from another NYC financial firm or a higher-paying quant/data shop nearby in Manhattan or Jersey City/Hoboken commuter range — that’s useful leverage.

Comparable Roles

• •

  Analytics Engineer (Wealth Management): $130,000 - $210,000 base

  • •Usually slightly below pure data engineering unless the role sits close to revenue reporting or BI platform ownership.
• •

  Data Platform Engineer: $160,000 - $240,000 base

  • •Often pays more when the job includes infrastructure ownership across ingestion frameworks and internal developer tooling.
• •

  Senior Data Engineer (Hedge Fund): $220,000 - $300,000+ base

  • •Typically higher than wealth management because hedge funds pay premiums for speed-sensitive data systems and direct trading impact.
• •

  Quant Data Engineer: $200,000 - $280,000+ base

  • •Strong upside if the role supports research pipelines or alternative data processing tied to investment decisions.
• •

  BI Engineer / Reporting Engineer: $120,000 - $175,,000 base

  • •Common in wealth management operations teams; usually lower than core data engineering unless tied to executive/client reporting platforms.

If you’re targeting New York wealth management specifically in 2026: aim high if you have cloud-native data engineering plus finance-domain experience. The market rewards engineers who can do more than move data — it rewards people who can make regulated financial data trustworthy enough for advisors, clients, auditors، and investment teams to rely on it.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.