Data Engineer II (HYBRID MA OR REMOTE)

The Hanover Insurance Group•Worcester, MA

2d•Hybrid

About The Position

For more than 170 years, The Hanover has been committed to delivering on our promises and being there when it matters the most. We live our values every day, demonstrating we CARE through our values, Sustainability initiatives and inclusive corporate culture. Our Claims Analytics team is currently seeking a Data Engineer II to join our growing team in our Worcester, MA office - Hybrid or remote work arrangement. This is a full-time, exempt position. POSITION OVERVIEW: Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. This role primarily will become proficient with all internal & external data produced and consumed by THG. The engineer will understand where the data is, basic data models and architecture, how to access and obtain data and how to manipulate and work with data to produce output – which may be reports, datasets or self-service reports. The ideal candidate is a self-motivated, adaptable, and composed collaborator that can learn quickly.

Requirements

Bachelor’s degree or higher in Data Science, Mathematics, Statistics, Computer Science, Information Systems, Business Information Technology, or equivalent
5+ years professional experience in data engineering, data architecture, or data analytics
Proficiency in the following languages: Advanced SQL, Python, Java, Scala (nice to have).
Proficient in building SQL stored procedures, ETL pipelines using Azure Synapse (Synapse Pipelines / Data Factory).
Holds active Azure DP-203 (Microsoft Certified: Azure Data Engineer Associate) or equivalent. Other Azure DP certifications or equivalents are also applicable
Familiarity in insurance data (Claims data is highly preferred).
Data Modeling: Creation of conceptual, logical, and physical data models for data objects, object attributes, and their relationships
Analytical Skills: Data Engineers work with large amounts of data that will include facts, figures, and number crunching. You will need to profile the data and analyze it to find conclusions.
Communication Skills: Data engineers are often called to present their findings or translate the data into an understandable document. You will need to write and speak clearly, easily communicating complex ideas.
Critical Thinking: Data engineers must look at the numbers, trends, and data and come to new conclusions based on the findings.
Attention to Detail: Data is precise. Data engineers must make sure they are vigilant in their analysis to come to correct conclusions.
Math Skills: Data engineers need advanced math skills to estimate numerical data. Insurance experience required as well as insurance products.
Debugging Skills: Data engineers need the ability to analyze issues with components in our data solutions and come up with remediation plans.
Proven experience in data engineering, implementation of best practices for data storage, access, integration, transformation, etc., within Azure
Proven experience using Databricks and delta live tables in a data engineering context
Experience in optimizing data pipelines to account for scale, performance, reliability, and cost efficiency
Demonstrated proficiency in relational, NoSQL, hierarchical, and entity relationship data modeling
Experience in creating easy to consume documentation of data processes and solutions to aid in knowledge transfer and continuity.
Experienced in agile development methodologies, developing high quality code, and DevOps best practices
Must be eligible to work in the United States without requiring sponsorship now or in the future (ie. lawful permanent residence or US citizen)

Nice To Haves

Proficiency in the following languages: Advanced SQL, Python, Java, Scala (nice to have).

Responsibilities

Develop scalable and efficient data models, data structures, and ETL pipelines that support analytics, reporting, and downstream consumption.
Consistently prioritize data quality by implementing validation checks, monitoring accuracy and completeness, and clearly documenting data pipelines and processes.
Work with no or minimum supervision on assigned projects and effectively manage multiple tasks or projects simultaneously.
Collaborate with business partners and cross-functional teams on data/reporting requests.
Be accountable for successful outcomes related to designing, developing, implementing, optimizing, and maintaining data pipelines and solutions.
Work with an ownership mindset to design, implement, and continuously improve data governance, controls, and standards, ensuring the platform is secure, compliant, and scalable.
Prioritize work effectively by balancing business impact, technical risk, and domain knowledge to deliver the highest‑value outcomes.