Description

Key Responsibilities

Data Strategy & Architecture

• Define and drive the enterprise data engineering strategy and roadmap.
• Design scalable data architectures supporting operational, analytical, and AI workloads.
• Establish standards for data modeling, data integration, metadata management, and data lifecycle management.
• Lead modernization efforts from legacy data environments to cloud-native data platforms.
• Ensure alignment with enterprise architecture, cybersecurity, and compliance requirements.

Data Platform Engineering

• Lead the design and implementation of enterprise data platforms utilizing cloud technologies and modern data architectures.
• Develop and maintain high-volume batch, streaming, and event-driven data pipelines.
• Build and manage data lakes, data warehouses, lakehouse architectures, and data products.
• Establish reusable frameworks and accelerators that improve engineering velocity and solution consistency.
• Drive platform automation through Infrastructure-as-Code and DataOps practices.

Leadership & Team Development

• Lead and mentor a team of Data Engineers, Data Architects, and Data Integration specialists.
• Establish engineering best practices and technical standards.
• Provide technical oversight, architecture reviews, and design guidance across projects.
• Foster a culture of innovation, accountability, continuous learning, and operational excellence.
• Support recruitment, onboarding, career development, and performance management activities.

Data Governance & Quality

• Implement enterprise data governance practices.
• Establish data quality frameworks, monitoring, observability, and remediation processes.
• Partner with data stewards and business stakeholders to improve trust in enterprise data assets.
• Ensure compliance with regulatory, privacy, retention, and security requirements.
• Define and monitor KPIs related to data quality, availability, and reliability.

AI & Advanced Analytics Enablement

• Build and optimize data environments that support AI, machine learning, and advanced analytics initiatives.
• Collaborate with data scientists and AI teams to operationalize models and data products.
• Support enterprise AI initiatives through governed, trusted, high-quality data pipelines.
• Establish patterns for feature engineering, model data preparation, and data consumption.

Delivery & Execution

• Manage delivery of multiple concurrent data engineering initiatives.
• Create project plans, estimates, resource forecasts, and delivery commitments.
• Drive agile delivery practices while maintaining governance and quality expectations.
• Identify risks, dependencies, technical debt, and remediation plans.
• Ensure predictable delivery, operational stability, and stakeholder satisfaction.

Stakeholder Engagement

• Work closely with executive leadership to align data investments with business objectives.
• Partner with engineering, product management, operations, and business teams to prioritize initiatives.
• Present technical recommendations, investment strategies, and progress updates to leadership audiences.
• Act as a trusted advisor for enterprise data strategy and modernization initiatives.

Required Qualifications

Education

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or related field.
• Master's degree preferred.

Experience

• 10+ years of experience in Data Engineering, Data Architecture, or related technology disciplines.
• 3+ years leading technical teams or enterprise-scale data initiatives.
• Demonstrated experience designing and delivering enterprise data platforms.
• Experience leading cross-functional teams in global delivery environments.

Technical Expertise

Strong experience in multiple areas including:

• SQL and advanced database technologies
• Python, Spark, Scala, or similar data engineering technologies
• ETL and ELT frameworks
• Data Lakes, Lakehouse, and Data Warehouse architectures
• Cloud platforms (Azure, AWS, or Google Cloud)
• Databricks, Snowflake, Synapse, Redshift, BigQuery, or equivalent technologies
• Real-time data processing and streaming architectures
• API-based integration and event-driven architectures
• CI/CD, DataOps, Infrastructure-as-Code, and automation practices

Preferred Qualifications

• Insurance industry experience.
• Experience supporting AI, Machine Learning, and Generative AI initiatives.
• Experience implementing enterprise data governance programs.
• Success leading large-scale cloud migration or data modernization programs.
• Experience with observability, operational monitoring, and reliability engineering

Key Skills

Education

Bachelor's degree