• Act as the technical reference for Data Architecture • Serve as the focal point for architectural decisions in high-complexity, cross-cutting strategic initiatives • Ensure alignment between business strategy and technical execution • Design reference architectures and guidance for corporate use, aligned with IT strategy and industry best practices, considering: • Functional and non-functional requirements • Internal and external data integrations • Security, scalability, performance, resilience, cost, and observability • Address vendor-agnostic and interoperable solutions across major cloud providers (Azure, AWS, GCP, and Oracle) • Improve internal data architecture processes, lead technology initiatives, facilitate discussions across IT disciplines, and influence technical decisions • Ensure reuse, rationalization, and best practices for a highly complex environment

• Designing data workflows, pipelines, and pathways to support ETL/ELT, while ensuring data-quality and exchange of sensitive information across AWS cloud and on-prem environments. • Implementing systems engineering principles to ensure the reliability, scalability, and maintainability of data layer systems, including backup and recovery strategies • Architecting, deploying, and operating data streaming technologies (Kafka, Redpanda, Kinesis, etc ) on Kubernetes with an emphasis on declarative definitions to reduce complexity in day-2 operations • Working with a platform team to implement best practices for configuration management, security, and performance optimization within Kubernetes and AWS environments • Working with a platform team to automate infrastructure provisioning, monitoring, and scaling using tools such as Terraform working with cybersecurity SMEs to incorporate best practices and techniques to defend our data layer against potential threats • Ensuring that our data layer systems are compliant with industry and DoD regulations, standards, and best-practices • Staying up-to-date with industry trends and emerging technologies related to Kubernetes, containerization, and data engineering

General Summary Senior Data Engineers are responsible for designing, developing, and maintaining scalable data platforms and pipelines that align with business objectives and drive operational efficiency. They analyze complex data challenges, architect reliable and secure data systems, and leverage advanced tools and technologies to meet evolving organizational needs. This role centers on the Databricks ecosystem and Python-based data engineering, with a strong emphasis on building production-grade data pipelines, optimizing data workflows, and enabling analytics and reporting capabilities across the organization. Key to success in this role is the ability to collaborate across cross-functional teams, apply critical thinking, and translate business requirements into actionable, high-impact data solutions. Familiarity with machine learning pipelines and feature engineering within a data engineering context is a plus. Analytics Focus Responsible for supporting the development of data pipelines and advanced queries leveraged by the business intelligence team as it develops and deploys dashboards and reports to support data-driven decision-making across the company. Essential Duties and Responsibilities - Design, develop, and maintain scalable, performant data pipelines using Python, PySpark, and Databricks, following Medallion architecture patterns (Bronze, Silver, Gold). - Build and optimize Delta Lake tables, manage schema evolution, and implement data quality checks across ingestion and transformation layers. - Develop and maintain Airflow DAGs for pipeline orchestration, scheduling, and monitoring. - Collaborate with product management and business stakeholders to interpret requirements and define data specifications. - Create and maintain semantic data layers and curated datasets for BI platforms (e.g., Power BI, QuickSight). - Analyze trade-offs between commercial and custom-built data solutions, offering technical guidance to decision-makers. - Troubleshoot and resolve issues across data workflows, pipeline failures, and infrastructure. - Support machine learning initiatives by building feature pipelines, data preparation workflows, and serving layers within the Databricks platform. - Ensure compliance with legal, regulatory, and internal governance requirements across data solutions. - Automate operational processes using CI/CD pipelines and infrastructure-as-code principles. - Establish and promote best practices for data engineering, pipeline development, and monitoring. - Partner with teams across the organization to translate business needs into technical data solutions. - Create and maintain detailed technical documentation including data lineage diagrams, pipeline architecture, and workflow documentation. - Serve as a subject matter expert in data engineering, pipeline optimization, Databricks platform capabilities, and data architecture. - Provide mentorship to junior team members, fostering professional growth and collaboration. - Communicate effectively with leadership and stakeholders regarding project status, risks, and milestones. - Stay current on emerging data engineering technologies and industry trends to drive continuous improvement and innovation. Requirements — Technical Skills - 7+ years of experience in data engineering or related roles. - Strong proficiency in Python and PySpark for data pipeline development and scripting. - Deep hands-on experience with Databricks, including Delta Live Tables (DLT), Databricks Workflows, Unity Catalog, and SQL Warehouses. - Strong knowledge of Medallion architecture (Bronze/Silver/Gold) within Databricks. - Solid understanding of Lakehouse and Delta Lake concepts, including ACID transactions, time travel, and schema enforcement. - Proficiency in Apache Airflow for workflow orchestration and scheduling. - Strong knowledge in relational databases, SQL query optimization, and multiple database systems. - Experience creating semantic data layers for BI platforms (e.g., Power BI, QuickSight). - Experience combining internal and external data sources for in-depth industry and business analyses. - Understanding of cloud computing platforms and integration with on-premise solutions. - Familiarity with CI/CD pipelines, version control (Git), and infrastructure-as-code practices. - Experience supporting ML pipelines and feature engineering within a data platform context is a plus. - Strong understanding of testing methodologies, including data validation, pipeline testing, and automation. - Experience working in regulated industries (e.g., insurance, banking, finance, healthcare). - Databricks certification preferred (e.g., Databricks Certified Data Engineer Associate or Professional). Requirements — Professional Skills - Outstanding analytical and problem-solving skills with the ability to simplify complex tasks and absorb new information rapidly. - Strong written and verbal communication skills, with the ability to convey technical information clearly to both technical and non-technical audiences. - Highly self-motivated, able to take initiative and manage time effectively to meet deadlines and deliverables. - Strong attention to detail with the ability to work independently with minimal supervision. - Ability to manage multiple projects simultaneously in a fast-paced, evolving environment. - Exceptional research skills with the ability to gather, analyze, and synthesize complex information quickly. - Passion for delivering exceptional quality and continuous learning of new technologies and frameworks. - Strong listening skills and the ability to build positive relationships with colleagues, stakeholders, and customers. - Commitment to creating an inclusive, team-oriented environment that aligns with company culture and values diversity. Work Environment - Remote: This position is a fully remote (work‑from‑home) opportunity. - Candidates must be currently located in the United States and authorized to work without sponsorship. - A suitable home workspace that is private and free from distractions is required. - Expected Work Hours: Work schedules are designed to meet both the needs of the role and the organization and may be adjusted as business needs evolve. - Travel: Possible travel to off‑site locations may be required for meetings, as needed. Salary Range : $90,000 - $135,000 and a comprehensive benefits package, please follow the link to our benefits page for details! EMPLOYERS Benefits and Perks About EMPLOYERS: As a dynamic, fast-growing provider of workers' compensation insurance and services, we are seeking a goal-oriented individual willing to put their ideas to work! We offer a positive, challenging work environment, combined with an opportunity to build your career as you help us grow our business, in innovative and imaginative ways that are uniquely EMPLOYERS®! Headquartered in Nevada, EMPLOYERS attributes its long-standing success to its most valuable resource, our employees across the United States. EMPLOYERS is known for the quality service and expertise we provide to our clients, and the exemplary work environment we provide for our employees. We live and breathe our core values: Integrity, Customer Focus, Collaboration, Initiative, Accountability, Innovation, and Personal Fulfillment. These are the pillars that support how we do business with our clients as well as how we treat each other! At EMPLOYERS, you’ll discover an energetic environment that inspires top achievement. As “America’s small business insurance specialist”, we have the resources, a solid reputation and an expanding nationwide identity to enrich your work/life and enhance your career. #LI-Remote Click our link to learn more! About EMPLOYERS

Part-Time Data Ingest Engineer – Contract Digital Public Library of America (DPLA) Remote | ~10–20 hrs/week | Fixed-term contract DPLA is looking for a part-time contractor to coordinate and maintain metadata ingest operations. This position is directly involved in maintaining DPLA’s ingestion process of harvesting, mapping, enriching, and indexing metadata from contributing partners. What you'll be doing - Running monthly ingest cycles across active partner contributions (harvesting, mapping, enrichment, indexing) - Coordinating with DPLA staff on metadata mapping and delivery - Monitoring pipeline reliability and addressing bottlenecks or single points of failure - Troubleshooting ingestion errors and coordinating resolution with DPLA staff - Supporting deployments and maintaining CI/CD pipeline health - Providing regular status updates to DPLA staff Technical environment - Pipeline: Scala, Apache Spark, Amazon EC2 and EMR, AWS S3, Apache Avro, Python scripts - Metadata: JSON-LD via DPLA MAP - APIs: Scala-based RESTful API on Elasticsearch 7, PostgreSQL auth backend - CI/CD: GitHub Actions, Docker, Terraform, AWS CodePipeline What we're looking for - Hands-on experience with Spark/Scala pipelines and AWS (EC2, EMR, S3) - Familiarity with cultural heritage metadata standards (RDF, JSON-LD, Dublin Core, MODS, or similar) and DAMS (CONTENTdm, etc.) - Experience working across metadata quality, pipeline ops, and infrastructure - Familiarity with GitHub-based collaborative workflows - Self-directed Details - 10–20 hours/week, flexible scheduling - $75 - $150 hourly rate (commensurate with experience) - An initial 3–6 month fixed-term contract, commencing April 1, with the possibility of extension. - Independent contractor arrangement (W-9/1099) - Must be legally authorized to work in the United States without company sponsorship About the Digital Public Library of America The Digital Public Library of America amplifies the value of libraries and cultural organizations as Americans’ most trusted sources of shared knowledge. We do this by collaborating with partners to accelerate innovative tools and ideas that empower and equip libraries to make information more accessible. DPLA is a 501(c)(3) non-profit. Visit https://dp.la.