Responsibilities: - Translate business needs and architectural guidance into detailed designs, data contracts, and implementation plans that break down large initiatives into actionable engineering tasks with reliable estimates - Create detailed pipeline designs covering schemas, transformations, partitioning, DLT configurations, orchestration, error handling, and observability that align with the platform architecture through close collaboration with the Data Architect - Lead implementation and guide junior engineers on design, coding standards, and best practices - Develop metadata-driven and configuration-driven pipeline patterns that reduce custom code and improve consistency - Make technical decisions that ensure reliability, performance, maintainability, and scalability. Ensure production readiness with monitoring, lineage, alerting, observability, CI/CD and documentation - Define and enforce engineering design patterns, coding standards, testing practices, and operational best practices - Evaluate and incorporate new technologies and Databricks capabilities that improve reliability, performance, or developer productivity - Validate new technologies with the Data Architect and operationalize them through documentation, examples, and enablement - Implement automated data quality checks, rule enforcement, and exception handling - Production support of both an existing and new platform including optimization of jobs, incident tracking and other analysis required for production - Lead resolution of complex production issues and deliver durable root cause fixes - Maintain SLAs for reliability, recovery, idempotency, performance, and cost efficiency - Mentor Level 2–3 engineers through pairing, design guidance, code reviews, and technical coaching Basic Skills: - Bachelor’s degree preferred; equivalent experience accepted - 10+ years of experience in data engineering (12+ without a degree) - 3+ years hands-on with Databricks (Delta Lake, DLT, Unity Catalog, workflow jobs) within the last 6 years - 4+ years building production-grade batch/streaming pipelines using PySpark, Spark Structured Streaming, Python, and SQL - Proven experience with data governance, schema evolution, data lineage, and secure access patterns - Proven 2 years’ experience with maintaining and sustaining data pipelines Preferred Skills: • Experience building metadata-driven or configuration-driven pipelines • Experience with data quality frameworks (DQX, Great Expectations, or equivalent) • Experience with observability, metrics and query performance analysis • Strong Spark optimization Work Conditions: • Team collaborative hours between 8am to 4pm EST • Remote corporate office/lab environment • Ability to travel 10% of the time

Role Description Frankenmuth Insurance is seeking a motivated individual to join our Finance Modernization & Automation team for a year-round internship opportunity. This position can be in office or remote. As an intern at Frankenmuth Insurance, you will have a chance to gain hands-on experience assisting the Finance Modernization & Automation team on a variety of projects. Primarily you will have the opportunity to assist with modernizing and automating financial data processes. The ideal candidate is curious, detail-oriented, and eager to apply analytical and technical skills to solve real-world business problems. This role will involve: - Collaborating with cross-functional teams - Addressing and supporting the company’s commitment to maintaining accurate, transparent, and compliant business practices Qualifications - Students should be currently enrolled and entering their sophomore-senior year in college - Preferred major is Data Analytics, Accounting, Auditing - Ability to organize data, manage multiple tasks and make decisions independently - Able to communicate effectively - Attention to detail Requirements - A minimum requirement for this position is the ability to work legally in the United States - No visa sponsorship/support is available for this position, including for any type of U.S. permanent residency (green card) process

• Become a trusted data and AI advisor to our clients • Work independently as part of a small team to solve complex data engineering use-cases • Design and implement AI-optimized data platforms • Build and evolve semantic and analytical layers that power various tools • Use modern platforms and tooling to deliver scalable solutions • Engineer modern ELT/ETL pipelines • Design modern data models • Write production-ready code in SQL, Python, and Spark • Apply AI-assisted data engineering techniques • Contribute to the evolution of our AI-forward data engineering and infrastructure practice • Collaborate with analytics, data science, and ML project teams

• Build, expand, and optimize data infrastructure in order to create the most accurate dataset of identities and their relationships • Develop and operate secure, scalable, and reliable data ingestion and ETL/ELT pipelines that meet product requirements • Design and maintain a data observability framework to ensure our data meets strict quality and freshness standards • Optimize data storage layer and build/maintain interfaces to enable scalable and fast data access to our data stores • Collaborate with product teams (squads) supporting their data platform needs for smooth delivery of existing and new products • Participate in call rotation for production issues, along with the rest of engineering • Mentor junior engineers and contribute to our evolving engineering best practices • Drive innovation by actively participating in our hackathons and doing proof of concepts • Develop functional subject matter expertise within various areas of identity fraud domain