Senior Software Engineer

William Blair•Chicago, IL

4h•Hybrid

About The Position

We’re seeking a versatile software engineer to design, modify, test, implement and maintain intelligent scalable software applications tailored to the needs of our Equities business. This role will involve collaborating throughout the software development lifecycle, ensuring compliance with firm standards, and leading projects from conception to deployment. Key Responsibilities: Design and maintain structured data pipelines using Databricks, Azure Data Factory, and Azure Synapse. Implement ETL/ELT workflows for structured and unstructured financial data across on‑prem and cloud platforms. Ensure data quality, lineage, governance, and observability across systems and pipelines. Build and deploy AI‑enabled solutions using Azure AI Services to support equity research, trading, and client service workflows. Productionalize Data Science POCs into secure, scalable, and monitored production services. Support migration of legacy data and application solutions (SQL, SSIS, Synapse, custom ETLs) to modern Azure cloud architectures. Design and optimize data models and analytical schemas (star/snowflake, partitioning, distribution strategies). Develop operational analytics dashboards to monitor pipeline health, SLAs, performance, and cloud spend. Work closely with PMs, developers, Data Science, and business stakeholders to define functional and technical requirements. Participate in Agile ceremonies and contribute to sprint planning and retrospectives. Lead testing of new and modified software, analyze issues, and resolve defects quickly. Document technical designs, integrations, and maintain operational playbooks/runbooks. Monitor industry trends in cloud data engineering, analytics, and AI; assess and recommend new technologies.

Requirements

Bachelor’s degree in information technology or related subject matter required
Hands-on expertise of at least 34-6 years with ADF, Spark, Databricks, Python, Azure Synapse, ADLS, and Azure Functions
Extensive background in Azure Synapse, including creating and managing pipelines, activities, and linked services.
Proficient in using ADF/Synapse to design full and incremental data loads from Azure and on‑prem data stores into ADLS.
Skilled in using Data API Gateways and REST‑based integrations for third‑party data extraction.
Capable of designing and building reusable ETL/ELT frameworks, including orchestrating pipelines in ADF/Synapse/Databricks.
Adept with Azure DevOps and YAML pipeline scripting for CI/CD automation.
Competent with Azure Key Vault, Logic Apps, Automation Runbooks, and cloud security best practices.
Strong SQL skills with proven experience in data modeling, schema design, analytical storage, and performance tuning.
Excellent communication, collaboration, and problem‑solving skills.

Nice To Haves

Hands‑on experience with Azure AI Services (OpenAI, Cognitive Services, embeddings, vector search).
Familiarity with Microsoft Fabric (future roadmap alignment).
Proficiency in ASP.NET, .NET Core, or C# is a strong plus.
Background in financial services or capital markets preferred.

Responsibilities

Design and maintain structured data pipelines using Databricks, Azure Data Factory, and Azure Synapse.
Implement ETL/ELT workflows for structured and unstructured financial data across on‑prem and cloud platforms.
Ensure data quality, lineage, governance, and observability across systems and pipelines.
Build and deploy AI‑enabled solutions using Azure AI Services to support equity research, trading, and client service workflows.
Productionalize Data Science POCs into secure, scalable, and monitored production services.
Support migration of legacy data and application solutions (SQL, SSIS, Synapse, custom ETLs) to modern Azure cloud architectures.
Design and optimize data models and analytical schemas (star/snowflake, partitioning, distribution strategies).
Develop operational analytics dashboards to monitor pipeline health, SLAs, performance, and cloud spend.
Work closely with PMs, developers, Data Science, and business stakeholders to define functional and technical requirements.
Participate in Agile ceremonies and contribute to sprint planning and retrospectives.
Lead testing of new and modified software, analyze issues, and resolve defects quickly.
Document technical designs, integrations, and maintain operational playbooks/runbooks.
Monitor industry trends in cloud data engineering, analytics, and AI; assess and recommend new technologies.