Lead Data Engineer

Mutual Bancorp and its Subsidiaries•Hyannis, MA

About The Position

The Data Engineer III will develop and scale our data ecosystem including our medallion architecture in Snowflake and our data integrations throughout the bank with the goal of building trusted semantic models that power enterprise reporting and self-service aligned with shared services across banks. This role will contribute to our evolving AI capabilities with a focus on ROI and time to insight and will partner closely with Finance stakeholders to turn complex financial data into reliable, decision-ready assets. This role is ideal for an engineer who understands the language of Finance, GL structures, net interest margin, regulatory reporting, budgeting and forecasting, and can translate those concepts into well-governed, high-performing data products in Snowflake for reporting, analytics, and self-service. This role will provide technical mentorship to Data Engineers I & II as they lead all aspects of technical delivery.

Requirements

Bachelor’s degree in computer science, Information Systems, Finance, Accounting, Mathematics, or a related field.
10+ years of professional experience in data engineering, data analytics, or a closely related role.
Demonstrated background working with Finance data and stakeholders - general ledger, financial consolidations, budgeting and forecasting, or bank/financial services reporting.
Experience supporting shared services across multiple entities.
Prior experience in banking, credit unions, or financial services, with exposure to core banking platforms and shared services across multiple entities.
Prior experience mentoring engineers and leading cross-functional data initiatives.
Expert-level proficiency in Snowflake, performance tuning, warehouse sizing, RBAC, Streams, Tasks, Snowflake Intelligence, Cortex, Dynamic Tables, integrated apps (Streamlit, others) and cost optimization.
Proven experience designing and implementing medallion architectures (Bronze/Silver/Gold) at enterprise scale.
Familiarity with Customer Master Data and Data Governance solutions and data with an ability to integrate that data across the bank’s ecosystem for analytics, reporting, and self-service.
Advanced SQL skills with the ability to write, tune, review, and troubleshoot complex queries against large financial and operational datasets.
Expert-level data integration experience - building and maintaining pipelines from source systems (core banking, ERP, GL, flat files, APIs) into a cloud data warehouse.
Deep expertise building semantic layers and views using tools such as dbt, Snowflake, BigQuery, and Matillion with a strong grasp of metric definitions, governance, and reusability to support downstream use cases within Snowflake Cortex/Intelligence and BI/visualization tools such as Power BI or Streamlit.
Strong proficiency in Python for data engineering, analytics, and data product development.
Experience building scalable data pipelines, performing advanced data transformations, and integrating with cloud data platforms such as Snowflake, Streamlit, BigQuery.
Familiarity with regulatory and financial reporting requirements such as Call Reports, SOX, CCPA, or CECL.
Proficiency with version control (Git) and CI/CD workflows.
Excellent communication skills, with the ability to explain technical concepts to Finance and executive audiences.
Familiarity with Jira/Agile methodology for project and task management.
Deep hands-on experience of data warehousing, master data management, data catalog, and data governance tools.
Must have cyber security awareness to protect the digital environment, the Bank, and customers.

Nice To Haves

Relevant certifications are a plus.

Responsibilities

Architect & Design: Design and develop Snowflake-native data systems and architecture, including our medallion architecture. Supporting application ingestion, API connections, and advanced reporting needs across Finance, Risk, Lending, and Retail.
Pipeline Engineering: Build ETL/ELT pipelines for incremental and initial data loads into Snowflake using tools such as Matillion, Snowpipe, Dbt, Tasks, and Dynamic Tables, along with external orchestration tools, integrating data from core banking, loan origination, GL, and third-party systems.
Master Data & Governance: Define, build, and manage customer and customer product solutions by consolidating and mastering golden records with match & merge, survivorship, householding, and legal entity relationships. establish data governance models, and enforce data quality, lineage, and consistency across systems. Aligning customer data models and hierarchies to support regulatory, operational, and analytical use cases
Semantic Layer: Lead the design, development, and implementation of our enterprise-level semantic layer, building models that serve as the single source of truth for all bank reporting.
Performance Optimization: Optimize Snowflake warehouse utilization and SQL queries for maximum performance and cost efficiency and conduct performance tuning on reports and underlying data models.
Stakeholder Partnership: Partner with Finance, FP&A, Accounting, Marketing, and other areas to translate business requirements into scalable data models and KPIs, writing advanced SQL for complex financial transformations, reconciliations, and performance-critical queries.
Quality Control & Code Review: Conduct peer reviews, enforce data engineering standards, support CI/CD practices, improve documentation, and ensure data products meet agreed acceptance criteria before release.
Troubleshooting: Resolve complex pipeline, integration, reconciliation, and deployment issues across the warehouse, integration, and reporting stack, coordinating with source system owners and infrastructure partners as needed.
Observability: Implement monitoring, alerting, and data quality checks to ensure data timeliness, completeness, accuracy, and one version of the truth in destination systems.
Governance & Standards: Establish and enforce best practices around data modeling, version control, CI/CD, and documentation, and collaborate with Information Security, Infrastructure, Digital, and Risk to ensure SOX, GLBA, and other regulatory requirements are met.
Artificial intelligence: Support the bank’s responsible, coordinated, and value-driven adoption of AI by helping establish the data foundations needed for analytical and AI use cases and supporting multiple Bank AI use cases at one time.
Mentorship: Become a domain expert on our banking and financial services business and provide technical mentorship to other team members to foster a culture of continuous learning.