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# Data Engineer

- **Company:** Sunrise Systems, Inc
- **Location:** Chicago, IL, United States (Remote available)
- **Salary:** $85,280.0 - $112,320.0
- **Contract:** Temporary contract
- **Skills:** Amazon Web Services, Data Analysis, Audit Trail, Data Architecture, Data Dictionary, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Security, Software Design Patterns, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Meta-Data Management, Performance Tuning, Query Optimization, SQL Databases, Apache Spark, Git, Data Lakes, Data Management, Software Version Control, Data Pipelines, Databricks
- **Published:** October 12, 2026
- **Apply:** https://www.dice.com/job-detail/0a62a47c-37b6-4a84-922f-4e738ea65f8c

## About the Role

This role combines deep technical expertise with domain knowledge of the insurance industry, leveraging cutting-edge technologies like Data bricks, Unity Catalog ABAC (Attribute-Based Access Control) and Row-Level Security (RLS), and modern data lake/lake house architecture patterns., Technical Skills

 * Hands-on experience with Databricks (Delta Lake, Spark, Notebooks, Jobs, DLT)
 * Deep understanding of data lake and lakehouse architecture patterns, including medallion architecture and query optimization
 * Strong expertise in ETL/ELT design patterns and distributed data processing
 * Experience implementing ABAC (Attribute-Based Access Control) and Row-Level Security (RLS) frameworks
 * Proficiency in Python and SQL for data pipeline development
 * Familiarity with Unity Catalog for data governance and metadata management
 * Experience with multi-region data architecture, federation, and data residency requirements
 * Required: AWS cloud platform expertise
 * Proficiency with version control (Git) and CI/CD pipelines
 * Understanding of data quality frameworks, validation patterns, and test-driven data development

Domain Expertise

 * Working knowledge of insurance industry terminology, business processes, and data models
 * Familiarity with common insurance data entities: Party, Claims, Fiduciary, etc.
 * Understanding of regulatory requirements relevant to insurance

Professional Skills

 * Strong problem-solving and analytical thinking abilities
 * Excellent communication skills to explain technical concepts to non-technical stakeholders
 * Collaborative mindset with experience working in cross-functional teams
 * Ability to manage multiple priorities and projects in a fast-paced environment

## Description

We are seeking an experienced Data Engineer to join our Innovation & Data Office on a contract basis, specifically focused on developing and maintaining scalable data platforms and pipelines within our Data Domains initiative. You will play a critical role in designing, building, and optimizing data infrastructure that supports insurance analytics, risk management, and business intelligence across Client's global operations., * Design and develop scalable ETL/ELT pipelines using Databricks, Delta Lake, and Auto Loader to ingest, transform, and load data from diverse sources

 * Architect and implement data lake/lakehouse solutions that support medallion architecture patterns (Bronze/Silver/Gold layers) with multi-region deployment capabilities
 * Implement and maintain secure data governance frameworks leveraging Unity Catalog, ABAC policies, and Row-Level Security (RLS) for fine-grained row and column-level access control across multi-region environments, ensuring compliance with insurance regulatory requirements
 * Build and optimize Delta Live Tables (DLT) pipelines with expectations and quality checks, supporting distributed deployments across multiple cloud regions
 * Collaborate with data product owners, analysts, and business stakeholders to define data requirements, SLAs, and performance optimization strategies
 * Implement data governance, quality controls, and audit trails to ensure data accuracy, integrity, and regulatory compliance across geographically distributed systems
 * Develop and maintain documentation, data dictionaries, and technical specifications for data pipelines and domain models
 * Troubleshoot data pipeline failures, performance bottlenecks, and data quality issues in production environments spanning multiple regions