Data Engineering Lead – Insurance

DXC Technology

. Define and drive the enterprise data engineering strategy and roadmap .

Posted 10/19/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing enterprise data platforms, leveraging cloud technologies and modern data architectures. Proven ability to lead technical teams, drive data governance practices, and ensure compliance with regulatory requirements while fostering innovation and operational excellence.

Highest-signal resume keywords

Data EngineeringCloud Platforms (Azure, AWS, Google Cloud)SQL and Advanced Database TechnologiesData Lakes and Data WarehousesTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ArchitectureETL and ELT FrameworksPythonSparkScalaReal-Time Data ProcessingCI/CDDataOpsInfrastructure-as-CodeObservability

Soft Skills

Strategic ThinkingDecision MakingStakeholder ManagementTalent DevelopmentCross-Functional Collaboration

Tools & Technologies

DatabricksSnowflakeSynapseRedshiftBigQuery

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree Preferred

Industry Keywords

Insurance Industry ExperienceEnterprise Data GovernanceAI and Machine Learning InitiativesCloud MigrationData Modernization

Tech Stack

Tools & technologies

Amazon RedshiftAWSAzureBigQueryCloudCyber SecurityETLPythonScalaSparkSQL

About the role

Key responsibilities & impact

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
Lead the design and implementation of enterprise data platforms using 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
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 innovation, accountability, continuous learning, and operational excellence
Support recruitment, onboarding, career development, and performance management activities
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
Build and optimize data environments supporting 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
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
Align data investments with business objectives alongside executive leadership
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

Requirements

What you’ll need

High school diploma or GED (required)
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
Strong experience with SQL and advanced database technologies
Strong experience with Python, Spark, Scala, or similar data engineering technologies
Experience with ETL and ELT frameworks
Experience with Data Lakes, Lakehouse, and Data Warehouse architectures
Experience with cloud platforms (Azure, AWS, or Google Cloud)
Experience with Databricks, Snowflake, Synapse, Redshift, BigQuery, or equivalent technologies
Experience with real-time data processing and streaming architectures
Experience with API-based integration and event-driven architectures
Experience with CI/CD, DataOps, Infrastructure-as-Code, and automation practices
Bachelor's degree in Computer Science, Information Systems, Engineering, or Data Science (Preferred)
Master's degree preferred
Insurance industry experience preferred
Experience supporting AI, Machine Learning, and Generative AI initiatives preferred
Experience implementing enterprise data governance programs preferred
Experience leading large-scale cloud migration or data modernization programs preferred
Experience with observability, operational monitoring, and reliability engineering preferred
Strategic Thinking
Technical Leadership
Decision Making
Stakeholder Management
Talent Development
Executive Communication
Continuous Improvement Mindset
Customer Focus
Results Orientation
Cross-Functional Collaboration

Benefits

Comp & perks

In-person collaboration prioritized with flexibility to support wellbeing, productivity, individual work styles, and life circumstances
Inclusive work environment
Reasonable accommodations for individuals with disabilities, disabled veterans, or wounded warriors