• Architecting, designing, developing, implementing and supporting various components for the enterprise data warehouse and associated data sources. • Works independently and provides support for reporting projects, as well as process and architectural recommendations. • Mentoring staff, demonstrating best practices, planning for business continuity and sustainability and working on projects that support Business Intelligence and Data Warehousing functions as assigned. • Works with the user community and reporting team to understand business data and data transformation requirements. • Converts those requirements to functional/technical specifications for database and integration design. • Manages requests and/or components of larger projects with appropriate documentation and status reports. • Architects data integration and transformation processes across on premise and cloud data sources. • Provides leadership and is a subject matter expert depending on area of focus. • Designs and develops data integration and transformation processes across on-premises and cloud data sources using enterprise tools such as Informatica, Denodo, SQL Server, SSIS, Oracle Data Integrator, and Boomi AtomSphere, selected based on environment and requirements. • Develops data models and database designs that leverage their knowledge of data warehouse modeling best practices. • Collaborates with other team members using scrum processes or other project management disciplines depending on area of focus. • Follows best practices for change and code management. • Provides supporting technical documentation that is written, organized and maintained. • Participates in ongoing stabilization, support and maintenance. • Provides on call support as needed for business continuity and operations. • Attends webinars, reads case studies and white papers, and participates in opportunities to further refine skills and stay apprised on functional area topics.

• Help define and mature data integration, data consolidation, MDM integration, and data platform design patterns across Integrichain. • Design, build, optimize, and operate Snowflake data models, pipelines, stored procedures, and high-volume data processing patterns. • Partner with MDM and Product teams to support HCO Master data ingestion, outbound extracts, cross-reference data, golden record consumption, survivorship outputs, and downstream publishing patterns. • Work with Product, Engineering, MDM, Data Science, DevOps, Security, and business stakeholders to align data solutions to enterprise priorities. • Use dbt or similar ELT tooling to develop reliable, maintainable, testable, and observable data pipelines. • Drive Snowflake performance tuning, warehouse sizing, workload management, cost tracking, and cost optimization practices. • Partner with Data Science leadership to rationalize and consolidate the enterprise data landscape across products, platforms, and acquired capabilities. • Define reusable data integration patterns for batch, micro-batch, near-real-time, and application-to-application data exchange. • Collaborate with cross-functional teams to understand business data needs, source-system realities, and enterprise application integration requirements. • Design scalable patterns for ingesting, transforming, mastering, and publishing data across operational and analytical use cases. • Help establish standards for data contracts, schema evolution, data quality, lineage, and data ownership. • Design and build data pipelines that load source data into Reltio MDM and extract mastered outputs from Reltio for downstream Snowflake, analytics, AI, and operational use cases. • Partner with MDM configuration and Product Management teams to translate HCO mastering requirements into data pipeline, mapping, validation, reconciliation, and publishing patterns. • Work with Reltio APIs, exports, crosswalks/XREFs, event-based integration patterns, and bulk load/extract mechanisms as needed to support inbound and outbound data flows. • Engineer integration patterns for HCO Master data, including party/entity, address, identifier, hierarchy, relationship, match/merge, survivorship, and golden record outputs. • Support source ingestion and reference data integration involving datasets such as HIN, DEA, NPI, NCPDP, 340B/PHS, channel outlet data, customer/account data, and other life sciences master/reference sources. • Develop validation and reconciliation processes to compare source data, Reltio mastered data, Snowflake curated data, and downstream consumption layers. • Help operationalize MDM outputs for business-facing data products, semantic models, reporting tables, APIs, and AI-ready datasets. • Design Snowflake database, schema, table, view, and semantic-layer patterns that support performance, governance, and maintainability. • Optimize Snowflake workloads using clustering, micro-partition awareness, warehouse sizing, query profiling, caching behavior, and workload isolation. • Implement Snowflake cost tracking and optimization practices, including warehouse utilization monitoring, inefficient query identification, and cost allocation by workload, team, or use case. • Build scalable SQL and Snowflake stored procedure logic for large-volume data processing and analytical workloads. • Apply secure Snowflake design patterns including RBAC, masking, access isolation, auditing, and environment separation. • Design, build, and maintain reliable ELT pipelines using dbt or comparable modern data transformation tooling. • Develop Python-based automation for API integration, file processing, metadata management, validation, orchestration support, and operational tooling. • Develop modular, tested, and reusable transformation models for raw, curated, mastered, and business-ready data layers. • Implement automated data quality checks, source freshness checks, reconciliation, logging, and exception-handling patterns. • Build orchestration-ready pipelines that support dependency management, restartability, incremental loads, and operational monitoring. • Collaborate with DevOps/SRE teams on CI/CD, deployment automation, environment promotion, and operational runbooks for data pipelines. • Spearhead logical and physical data modeling efforts for enterprise analytical, operational, MDM, and AI-ready datasets. • Design models that balance normalization, dimensional modeling, medallion/lakehouse concepts, and application-specific consumption needs. • Create denormalized reporting and semantic-model-ready structures that simplify business consumption and reduce ambiguity for AI/LLM use cases. • Process and optimize large data volumes in Snowflake using efficient SQL, PL/SQL-style procedural logic, Snowflake Scripting, and performance-aware design. • Create reusable patterns for historical tracking, snapshots, audit columns, data versioning, and lifecycle management. • Ensure data models support downstream BI, AI/ML, semantic models, data apps, MDM Explorer/Entity 360 use cases, and enterprise reporting.

• Co-define and evolve the data platform roadmap in collaboration with architecture and engineering teams • Translate technical and business requirements into epics, user stories, and technical backlog items • Work closely with Data Engineers and Architects on: data models and architectures (batch/streaming) data pipelines and ingestion frameworks storage (e.g. data lake / data warehouse) and processing layers • Support design and implementation of platform components for: machine learning workflows (MLOps, feature stores, model lifecycle) data observability, lineage, and quality monitoring • Ensure datasets are reliable, well-structured, and ready for analytics and ML use cases • Participate in technical discussions and architecture decisions (not just coordination) • Drive delivery in an agile setup (planning, backlog refinement, prioritisation) • Communicate technical concepts in a clear way to non-technical stakeholders

Role Description This role will primarily become proficient with all internal & external data produced and consumed by Hanover’s Core Commercial and Specialty businesses. This role will drive complex projects independently as well as under the mentorship of experienced engineers. The Associate Data Engineer will collaborate with business partners and cross-functional teams on data and reporting requests. The assignments will become broader in nature, usually requiring originality and ingenuity. This role will be accountable for successful outcomes related to designing, developing, implementing, optimizing, and maintaining data integrations, pipelines and solutions. IN THIS ROLE, YOU WILL: - Complete data integration and build scalable ETL pipelines - Learn data engineering best practices for data storage, integration, transformation, and access within cloud and on-premises platforms - Gain a strong understanding of Medallion Architecture and Data Lake best practices - Ensure data security and compliance within industry standards - Develop and maintain documentation for data integration and ETL solutions - Build solutions within Azure Data Services (Azure Synapse Analytics, Azure SQL Database, Azure Data Lake Storage) - Create solutions using pySpark/SparkSQL, and Delta Tables - Collaborate with data scientists, analysts, and stakeholders - Work within agile development methodologies - Occasional Power BI report creation for data delivery - Utilize best practices for CI/CD Qualifications - Bachelor’s degree or higher in Data Science, Mathematics, Statistics, Computer Science, Information Systems, Business Information Technology, or other related fields - 0-2 years professional experience in data engineering, data architecture, and/or data analytics - Beginner proficiency in SQL and Python - Strong problem-solving, communication, and collaboration skills - Critical thinking and attention to detail - Must be eligible to work in the United States without requiring sponsorship now or in the future (i.e. US Citizen or green card holder) Requirements - Some experience working with Azure, AWS, GCP, or other cloud - CI/CD experience with ADO or Github - Azure Synapse Analytics and/or Microsoft Fabric experience - PowerBI experience - Insurance industry experience Benefits - Medical, dental, vision, life, and disability insurance - 401K with a company match - Tuition reimbursement - PTO - Company paid holidays - Flexible work arrangements - Cultural Awareness Day in support of IDE - On-site medical/wellness center (Worcester only)