EP Wealth Advisors (EPWA) is a wealth management advisory firm with over $49.1 billion as of June 30, 2026, serving predominately high net worth individuals. EPWA fosters an inclusive environment that offers opportunities for our associates to learn, grow and enhance their skills to take on new challenges to progress in their professional careers.
Summary
EP Wealth Advisors is building and scaling a modern enterprise data platform centered on Snowflake and Microsoft Azure. We are seeking a seasoned, hands-on Data Engineer to help design, build, operate, and continuously improve the data pipelines and engineering frameworks that power enterprise analytics, reporting, operational workflows, and future AI capabilities.
This role will be a key technical contributor within the Data & AI Platforms organization. The Data Engineer will work across Snowflake, Azure, Windmill, enterprise APIs, and internal business systems to create reliable and reusable data ingestion, transformation, and integration capabilities.
The ideal candidate combines strong data engineering fundamentals with deep hands-on Snowflake experience, advanced SQL and Python skills, cloud engineering expertise, and a modern engineering mindset that incorporates automation and AI-assisted development.
Key Responsibilities
Data Engineering and Snowflake
- Design, build, test, deploy, and maintain scalable ELT/ETL pipelines that ingest and transform data from enterprise applications and external data providers into Snowflake.
- Develop high-quality Snowflake data models, schemas, transformations, and reusable data engineering components supporting analytics, reporting, operational workflows, and AI use cases.
- Build and maintain pipelines from source systems (Salesforce, Tamarac, API logs) into clean analytics layers
- Write and optimize complex SQL and Python-based data processing workloads.
- Apply Snowflake engineering best practices for performance, scalability, security, reliability, and cost management.
- Work with Snowflake capabilities such as tasks, streams, Snowpark, data sharing, automated ingestion, and related platform features as appropriate.
- Diagnose and resolve data pipeline, query performance, data quality, and production reliability issues.
Data Integration and Orchestration
- Develop and maintain data ingestion and orchestration workflows using Windmill and other appropriate integration technologies.
- Integrate data from APIs, databases, files, SaaS platforms, custodians, and other enterprise data sources.
- Build reusable ingestion patterns and frameworks rather than one-off integrations wherever practical.
- Design pipelines with appropriate scheduling, dependency management, retries, error handling, logging, monitoring, and alerting.
- Work with enterprise systems such as Salesforce and financial-services platforms supporting CRM, portfolio management, financial planning, custodial data, and related business processes.
Azure and Cloud Engineering
- Build and support data engineering solutions within the Microsoft Azure ecosystem.
- Work with relevant Azure services for storage, identity and access management, secrets management, networking, monitoring, automation, and integration with Snowflake.
- Implement secure authentication and service-to-service integration patterns.
- Participate in CI/CD, source control, testing, deployment automation, and environment-management practices.
- Partner with Security and Infrastructure teams to ensure cloud data solutions meet enterprise security and operational standards.
Data Quality, Governance, and Reliability
- Implement automated data validation, reconciliation, testing, monitoring, and auditing to ensure data accuracy, completeness, timeliness, and reliability.
- Build data pipelines that are observable and operationally supportable, with clear logging and failure recovery.
- Support data lineage, metadata management, access controls, and enterprise data-governance practices.
- Help establish trusted source-of-truth data sets and consistent business definitions across systems.
- Apply appropriate data-security and access-control standards for sensitive financial and client information.
- Identify, troubleshoot, and resolve data issues including data quality, integrity, latency, and security concerns; apply monitoring and operational best practices to keep pipelines reliable and performant.
- Contribute to data quality and governance practices, including profiling datasets, defining quality rules, and establishing monitoring/remediation approaches.
- Produce clear technical documentation covering pipelines, transformations, dependencies, data models, and operational procedures.
AI-Enabled Engineering
- Use modern AI-assisted software-development tools to improve engineering productivity, code quality, testing, documentation, troubleshooting, and development velocity.
- Apply AI responsibly when developing Python, SQL, integrations, and data-engineering solutions while adhering to security, privacy, and data-governance requirements.
- Evaluate opportunities to leverage Azure and Snowflake AI capabilities where they provide practical value to data engineering, analytics, or enterprise workflows.
- Exposure to or experience with AI/ML data pipelines, including RAG architectures, vector databases, or embeddings workflows
- Familiarity with agent-based systems, MCP integrations, or LLM-powered applications is a strong plus collaboration
- Partner closely with data analysts, application teams, architecture, security, compliance, and business stakeholders to translate data requirements into reliable technical solutions.
- Collaborate with external technology and implementation partners when appropriate while helping EP develop strong internal data engineering capabilities.
- Participate in technical design reviews, sprint planning, code reviews, architecture discussions, and production support.
- Mentor other engineers and help establish pragmatic engineering standards and reusable patterns for the Data & AI Platforms organization.
Qualifications
