Director of Data Strategy

About CareScout Services:

Join us on a mission to simplify and dignify the aging experience. We are the children, siblings, neighbors, and friends of those navigating the fragmented and confusing system of long-term care. Our team is ferociously curious and relentless in our pursuit of a better system – and we are deeply committed to a sense of belonging for all, in all phases of life.

We’re creating a new experience for care seekers and their families, bringing together long-term care options, resources, education, and human support into one place. We work hard, we have fun, we care about each other, and we share the mission. If this sounds like a place where you could thrive, join us!

CareScout is a division of Genworth Financial, Inc, a Fortune 500 provider of products, services and solutions that help families address the financial challenges of aging.

Job Summary:
The Director of Data Strategy will lead the development and execution of our data strategy, with a strong focus on building and leveraging a robust data lake on the Azure platform. This role will be instrumental in enabling advanced analytics, driving marketing integrations, and fostering a data-driven culture. The ideal candidate will have deep technical expertise in Data Engineering and a proven track record of delivering impactful data solutions that enhance marketing effectiveness and overall business performance.

Responsibilities:
Strategic Data Leadership:

• Develop and implement a comprehensive data strategy aligned with business objectives

• Identify opportunities to leverage data for competitive advantage, particularly in marketing and customer engagement.

• Provide thought leadership on data trends, technologies, and best practices..

• Develop and implement an enterprise data model & data dictionary to standardize our reporting needs.

• Data Lake Development and Management:

• Design, build, and maintain a scalable and secure data lake on Azure.

• Design and build scalable ETL pipelines, establish data ingestion, transformation, and storage patterns.

• Architect data warehouse schema (Star, Snowflake, Galaxy)

• Optimize data lake performance and ensure data quality and integrity.

• Establish balancing and reconciliation queries to ensure Data warehouse is stable after failures, numbers and records match to expectations to ensure quality, consistency and availability

• Enabling the business to leverage Artificial Intelligence and Machine Learning:

• Work with the product managers, and the business stakeholders to identify opportunities to use AI/ML solutions.

• Stay abreast on trends in AI/ML

• Ensure data availability and quality

• Establish data governance for ML – Define & Implement policies and procedures that ensure quality, consistency and security for ML models

• Manage data pipelines for ML

• Address data privacy and ethical considerations

• Collaborate with data scientists and engineers

• Establish ML model lifecycle management – develop process for model development, deployment, monitoring and maintenance.

• Enable model operationalization (MLOps)- adopt MLOps practices to manage process

• Measure and monitor ML model performance: Establish metrics and processes for measuring and monitoring the performance of ML models in production

Marketing Data Integration and Activation:

• Lead the integration of marketing data from various sources (CRM, marketing automation platforms, and other operational systems) into the data lake.

• Enable the use of data lake data for targeted marketing campaigns, customer segmentation, and personalized experiences.

• Collaborate with marketing teams to develop and implement data-driven marketing strategies.

• Oversee the activation of data to marketing platforms, and ensure a smooth flow of information.

• Data Governance and Compliance:

• Establish and enforce data governance policies and procedures, ensuring compliance with relevant data privacy regulations.

• Implement data security measures to protect sensitive data within the Azure environment.

• Define and manage data access controls and permissions.

Data Analytics and Insights:

• Partner with analytics teams to leverage the data lake for advanced analytics, reporting, and business intelligence.

• Promote the use of data analytics to drive informed decision-making across the organization.

• Project Execution