Rand Mutual Assurance (RMA) is seeking an experienced Data Engineer Manager to lead the design, development, and optimization of data pipelines and data sourcing strategies. This role will be key in establishing how partner data is integrated into the RMA environment and ensuring that data solutions are robust, scalable, and aligned with governance standards. The successful candidate will collaborate across business and technology teams to deliver high-quality, analytics-ready data that supports customer acquisition insights, operational efficiency, and business performance reporting.

Key Responsibilities

• Develop Data Sourcing Strategy: Define and implement the approach for ingesting and integrating partner data into the RMA data environment.
• Pipeline Design & Optimization: Architect and enhance end-to-end data pipelines for ingestion, transformation, and delivery to ensure performance, scalability, and reliability.
• ETL Development: Build, maintain, and optimize ETL processes that convert raw data into structured, analytics-ready formats for reporting and business intelligence.
• Automation & Efficiency: Automate manual workflows, streamline data processes, and enhance the scalability and maintainability of data systems.
• Analytics Enablement: Develop data tools and frameworks that support business insight generation, customer acquisition analysis, and operational reporting.
• Data Quality & Governance: Implement testing, validation, and quality assurance measures to ensure data accuracy and compliance with governance and regulatory standards.
• Collaboration & Stakeholder Management: Work closely with data analysts, data scientists, and business units to define requirements and deliver actionable data solutions.
• Thought Leadership: Serve as the subject-matter expert on data engineering standards, governance alignment, and best-fit data architectures within the RMA environment.

Key Skills & Experience

• Proven experience in data engineering management, leading or mentoring teams delivering complex data solutions.
• Strong technical expertise in data pipeline design, ETL development, and data warehousing.
• Proficiency in SQL, Python, Azure Data Factory, Databricks, or equivalent data engineering tools and frameworks.
• Demonstrated ability to automate processes and improve data system scalability and performance.
• Solid understanding of data governance, data quality management, and compliance frameworks.
• Experience collaborating with data science and business intelligence teams to translate business needs into technical solutions.
• Excellent problem-solving, communication, and stakeholder-management skills.

Preferred Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field.
• Advanced certifications in cloud data engineering (e.g., Microsoft Azure Data Engineer, AWS Data Analytics, GCP Professional Data Engineer) are advantageous.