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# Senior Data Engineer

- **Company:** Epik Solutions
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Salary:** $124,800.0 - $166,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Information Engineering, Python (Programming Language), Reference Data, Data Access Layer, Azure Data Lake, SQL Databases, Azure Data Factory, GitHub Copilot, Apache Spark, Build Management, Data Management, Virtual Agents, Data Pipelines, Databricks
- **Published:** August 11, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=249edf5dfe3b0f0c

## About the Role

* 4+ years of hands-on data engineering with strong Python, SQL, Spark/Databricks, and Azure (ADF, ADLS, Synapse).
 * Domain experience with investment, asset management, or capital-markets data (portfolios, securities, risk, performance, or reference data).
 * Strong data modelling (dimensional, SCD, medallion architecture) and pipeline orchestration.
 * Agentic AI / GenAI-assisted development experience (e.g., GitHub Copilot).

Preferred

 * CFA charterholder or active CFA candidate (Level I-III) - or equivalent demonstrated investment-domain fluency.

Who You Are

A younger, high-potential professional with an outstanding track record; collaborative, curious, and comfortable working directly with senior client stakeholders in a people-first, partnership culture.

## Description

We are building an elite, domain-deep data engineering team for a leading global asset & wealth management client. This is not a body-shop role - you will work shoulder-to-shoulder with the client's Data & AI leadership on investment, risk, and asset-management data platforms. We are looking for exceptionally strong, high-quality engineers who combine modern data engineering craft with genuine capital-markets understanding., * Design and build cloud data pipelines on Azure Data Lake, Azure Data Factory (ADF) and Databricks (ADB) across ingestion * curation (Silver) * Data Access Layer (Gold).

 * Implement SCD Type-2, transformation logic, and DAL standardization for investment, risk and AUM datasets.
 * Model and engineer data for asset management, portfolio risk, and performance workflows (e.g., Risk, FRM-GA, BarraOne, AUM reporting).
 * Build data quality, validation and reconciliation frameworks (source-to-target, QA evidence).
 * Collaborate with data scientists and quants to deliver AI-ready, governed data foundations, including agentic / GenAI-assisted engineering.


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