Data Engineer

Merchants Bank of Indiana•Carmel, IN

11h

About The Position

Our business technology team is enhancing its data engineering capabilities with focus in financial services data including accounting, finance, and broader Enterprise Data initiatives. We are seeking a skilled Data Engineer, along with at least three years of experience, who will design, build, and maintain scalable data pipelines, integrations, and Lakehouse architectures utilizing Python, Microsoft Fabric, Azure DevOps, Jira and more.

Requirements

Bachelor’s or Master’s degree in computer science, data analytics, or a related field.
3+ years of experience in data engineering or cloud ETL development.
Proficient in Python for data engineering, automation, and integration tasks.
Experience designing and managing CI/CD pipelines (Azure DevOps, GitHub, or similar).
Experience with version control (preferably Git-based) from managing code, pipelines, and deployment artifacts.
Hands-on experience integrating systems (Bulk API, REST API, or connector-based ingestion).
Experience with data quality frameworks and data governance concepts.

Nice To Haves

Experience working with investment, capital markets, or financial services data.
Familiarity with Git-based workflows, DevOps pipelines, or Fabric deployment processes.
Power BI and semantic model support experience.
Experience with cloud-based orchestration or event-driven patterns.

Responsibilities

Design, build, and optimize ETL/ELT pipelines utilizing Microsoft Fabric Notebooks (Python) and Fabric Pipelines.
Utilize CI/CD pipelines (Azure DevOps, Git-based workflows, Fabric deployment pipelines, etc.) to automate code deployments, testing, and release processes.
Maintain version control across code, pipeline definitions, SQL models, and integration scripts.
Build and maintain integrations (initially Salesforce) for syncing data between source systems and the enterprise data warehouse in Microsoft Fabric.
Architect and manage Lakehouse (Bronze/Silver/Gold) structures aligning with enterprise medallion best practices.
Develop scalable data models for investment reporting, portfolio analytics, and capital markets insights.
Ensure schema alignment, consistency, and auditability across all inbound data flows.
Implement ongoing monitoring, validation, and reconciliation to support trusted investment and risk reporting.
Maintain metadata, data dictionaries, and technical documentation in alignment with Enterprise data governance.