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

• Develop project plans and estimates

• Develop milestones, and identify risks

• Establish project control and communicate progress, risks and mitigation plans

Team Leadership and Collaboration:

• Build and lead a high-performing data strategy team with expertise in Azure data services.

• Hire coach and mentor staff and contracting team members

• Collaborate with cross-functional teams, including product management, marketing, operations, sales, and analytics, to achieve data-related objectives.

• Manage relationships with vendors.

• Plan and manage budget

Qualifications:

• Bachelor's degree in computer science, data science, or a related field (Master's degree preferred).

• Minimum of 10 years of experience in data strategy, data engineering, or a related field, with a focus on Azure data services.

• Proven experience building and managing data lakes on the Azure platform.

• Strong expertise in Data Lake technologies such as Databricks or Azure Synapse, or Snowflake.

• Experience with marketing data integration and activation.

• Strong understanding of data governance, data security, and compliance principles.

• Excellent leadership, communication, and interpersonal skills.

• Strong analytical and problem-solving abilities.

• Experience in managing globally distributed teams of data engineers as staff and contractors

Desired Skills:

• Experience with Databricks or Snowflake or Azure Syanpse/Fabric

• Experience in Extract Transform and Load (ETL) tools like Fivetran or others like Azure Data Factory, AWS Glue, Apache airflow

• Hands-on experience in architecting data warehouse using Star, Snowflake or Galaxy schema

• Proficiency with at least one cloud platform (Azure, AWS, or GCP)

• Experience in data security

• Strong understanding of relational databases (MySQL, Postgress, SQL Server, MongoDB)

• Advanced SQL skills

• Experience in data dictionary, data quality and lineage tools

• Experience in machine learning frameworks: Pytorch, MLFlow, and or Tensorflow

• Experience with marketing automation platforms (e.g., Iterable, Dynamic 360).

• Experience with CRM platforms (e.g. Dynamics 365).

• Experience with API integrations.

• Experience with data visualization tools like Power BI or Tableau

• Experience with Data visualizations tools such as Power BI.

• Understanding of ML concepts and algorithms (supervised learning, unsupervised learning, deep learning)

• Understanding of ML Ops best practices

• Understanding of API concepts with experience in integrating data from diverse sources

• Experience in Metadata Management, and Data Modeling