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Lead Data Engineer

Job in Barnstable, Barnstable County, Massachusetts, 02630, USA
Listing for: Mutual Bancorp
Full Time position
Listed on 2026-07-08
Job specializations:
  • IT/Tech
    Data Engineering, Data Warehousing
Salary/Wage Range or Industry Benchmark: 99792 USD Yearly USD 99792.00 YEAR
Job Description & How to Apply Below

1500 Iyannough Road, Hyannis, MA 02601, USA

  • Pay or shift range: $99,792.99 USD to $ USD. The estimated range is the budgeted amount for this position. Final offers are based on various factors, including skill set, experience, location, qualifications and other job-related reasons.
Description

SUMMARY: 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.

ESSENTIAL

JOB FUNCTIONS & 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, house holding, 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…
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