Senior Data Engineer

Washington Trust•WESTERLY, RI

7h•$165,000 - $195,000•Hybrid

About The Position

Washington Trust is seeking an accomplished and forward‑thinking Senior Data Engineer to support and advance the organization’s enterprise‑wide data and analytics capabilities. As a key member of our Management Information Systems (MIS) function, the Senior Data Engineer will design, build, and modernize scalable data platforms that power decision‑making across all business lines and corporate functions. This role serves as a technical leader and trusted partner to stakeholders throughout the enterprise, driving the evolution of our cloud‑based data environment, ensuring data quality and governance, and enabling advanced analytics, reporting, and business intelligence solutions. The Senior Data Engineer will play a critical role in shaping Washington Trust’s data architecture strategy, mentoring team members, and delivering innovative solutions that support operational excellence, customer insights, and enterprise performance. This is a hybrid position with periodic on-site work in our office in Westerly, RI.

Requirements

Bachelor’s degree in computer science, data science, information technology, or a related field.
7+ years of progressive data engineering experience, preferably in banking, financial services, or regulated industries.
Deep expertise in Azure data services (ADF, Synapse, ADLS, Azure SQL) and strong familiarity with Microsoft Fabric.
Proven experience designing and integrating APIs, Logic Apps, Functions, SaaS systems, and modern cloud data architectures.
Advanced proficiency in SQL/T‑SQL and Python for data engineering, automation, and analytics.
Strong understanding of data modeling (dimensional, relational, domain‑driven), warehousing concepts, and performance optimization.
Experience delivering enterprise BI solutions using Power BI, including models, datasets, and governance layers.
Background with DevOps practices including automated builds, testing, and deployments.
Demonstrated ability to translate complex enterprise data needs into scalable architecture and efficient engineering solutions.
Strong knowledge of data governance, metadata management, data lineage, and data quality frameworks.
Exceptional problem‑solving, analytical, and critical‑thinking abilities.
Effective communicator capable of engaging with executives, technical teams, and diverse business stakeholders.
Collaborative leader with an ownership mindset and a passion for innovation and continuous improvement.
Comfortable operating in a fast‑paced, evolving environment where enterprise data capabilities are growing rapidly.

Nice To Haves

Master’s degree or relevant certifications (Azure, Data Engineering, Cloud Architecture) is a plus.
Familiarity with AI/Machine Learning concepts and cloud‑enabled analytics tools (Azure ML, Fabric Data Science experiences).

Responsibilities

Serve as the primary architect and builder of Washington Trust’s modern data ecosystem, establishing the foundational patterns, standards, and frameworks for all future data engineering initiatives.
Act as the technical authority for data engineering, providing architectural direction and influencing data strategy across technology and business teams.
Lead modernization initiatives using Azure Data Services and Microsoft Fabric.
Architect, implement, and maintain scalable data models, data lakes, data warehouses, and semantic layers that support enterprise data needs.
Design, develop, and optimize end‑to‑end data ingestion, transformation, and integration pipelines spanning multiple business domains.
Collaborate with leaders and teams across the organization to understand data needs and translate business requirements into robust technical solutions.
Champion a data‑product mindset, creating reusable, governed, and well‑documented data assets that support analytics, risk, finance, and operations.
Develop and deploy high-performing, maintainable code using SQL, T‑SQL, Python.
Implement Continuous Integration / Continuous Deployment pipelines and DevOps best practices to improve efficiency, reliability, and automation across data environments.
Champion data quality by implementing standardized validation rules, lineage tracking, and monitoring frameworks.
Develop, support, and enhance enterprise data visualization and reporting solutions using Power BI, including dataset creation, semantic modeling, and dashboard optimization.
Collaborate with Enterprise Risk & Technology teams to uphold data standards, regulatory expectations and protection protocols.
Enforce change management protocols, ensuring deployments, enhancements and modifications follow approval workflows, documentation standards, testing processes and compliance requirements.
Mentor analysts, developers, and data users to elevate data literacy and engineering practices enterprise‑wide.