Senior Manager, Data Engineering

Independence Pet Group•Chicago, IL

20h•$169,279 - $211,599•Onsite

About The Position

Independence Pet Holdings, established in 2021, is a corporate holding company managing a diverse portfolio of modern pet health brands and services, including insurance, pet education, and lost recovery services throughout North America. The company aims to simplify the pet parenting journey and improve pet well-being through its solutions. Independence Pet Holdings operates with a full stack of resources, capital, and services to support pet parents, utilizing a multi-brand and omni-channel approach that includes its own insurance carrier, insurance brands, and partner brands.

Requirements

Experience with Azure Databricks, ADLS Gen2, and Azure-native services for data ingestion.
Experience scaling batch and real-time ingestion pipelines.
Experience defining and standardizing data ingestion patterns, frameworks, and orchestration standards.
Experience defining roadmaps for data ingestion capabilities.
Experience driving performance tuning, cost optimization, and reliability of data pipelines.
Experience owning MDM strategy, architecture, and implementation.
Experience defining and managing golden record creation.
Experience driving alignment with product, analytics, and business stakeholders.
Experience partnering with business and governance teams.
Experience overseeing the development and implementation of probabilistic matching algorithms for entity resolution.
Experience leading development of taxonomy-driven data models and hierarchies.
Experience promoting data lakehouse architecture principles and modular data design.
Experience ensuring alignment with enterprise data governance and security standards.
Experience building and leading high-performing teams of data engineers.
Experience defining team structure, capacity planning, and hiring strategy.
Experience influencing overall data architecture, platform standards, and technology decisions.
Experience ensuring data platforms support critical business outcomes.
Experience driving agile delivery, sprint planning, and engineering best practices.
Experience establishing CI/CD pipelines, testing strategies, and DevOps practices.
Experience implementing robust data quality frameworks, monitoring, and SLAs.
Experience ensuring compliance with data privacy, security, and regulatory requirements.

Responsibilities

Own the design and implementation of scalable data ingestion frameworks using Azure Databricks, ADLS Gen2, and Azure-native services.
Oversee and scale batch and real-time ingestion pipelines (e.g., via Event Hub, Service Bus, Kafka).
Define and standardize reusable ingestion patterns, frameworks, and orchestration standards.
Define the roadmap for data ingestion capabilities aligned to product and business priorities.
Drive performance tuning, cost optimization, and reliability of data pipelines.
Own the MDM strategy, architecture, and implementation across key business domains.
Define and manage golden record creation across domains (customer, product, provider, etc.).
Drive alignment with product, analytics, and business stakeholders to ensure consistent, trusted data across the organization.
Partner with business and governance teams to align MDM strategy with organizational goals.
Oversee the development and implementation of probabilistic matching algorithms for entity resolution.
Lead development of taxonomy-driven data models and hierarchies.
Promote data lakehouse architecture principles and modular data design.
Ensure alignment with enterprise data governance and security standards.
Build and lead a high-performing team of data engineers, including senior engineers and/or technical leads.
Define team structure, capacity planning, and hiring strategy to support platform growth.
Influence overall data architecture, platform standards, and technology decisions.
Ensure data platforms directly support critical business outcomes, including product capabilities, analytics, and operational workflows.
Drive agile delivery, sprint planning, and engineering best practices.
Establish CI/CD pipelines, testing strategies, and DevOps practices.
Implement robust data quality frameworks, monitoring, and SLAs.
Ensure compliance with data privacy, security, and regulatory requirements.