Director of Data Engineering @ Genworth Financial

Data & Analytics (1)

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 Engineering 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

For candidates based in the New York City Metropolitan area, the base salary pay range for this role starts at a minimum rate of $190,000 up to the maximum of $240,000. An employee’s pay position within the base salary pay range will be based on several factors at the time of this job posting including but not limited to geographic location, experience, and qualifications. In addition to your base salary, you will also be eligible to participate in an incentive plan. The incentive plan is based on performance and the target earning opportunity is 20% of your base compensation. The final determination on base pay for this position will be based on multiple factors to ensure pay equity within the organization.

WHY CARESCOUT SERVICES?
• We have a real impact on the lives of the people we serve
• We work on challenging and rewarding projects
• We give back to the communities where we live
• We offer competitive benefits including:
o Medical, Dental, Vision, Flexible Spending Account options beginning your first day  
o Generous Choice Time Off your first full year
o 12 Paid Holidays
o 40 hours of volunteer time off
o 401k Account with matching contributions
o Tuition Reimbursement and Student Loan Repayment
o Paid Family Leave
o Child Care Subsidy Program

Director of Data Engineering

Genworth Financial

Compensation Overview

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

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