Data Engineering Lead

Job Description:

Position Summary

The Data Engineering Lead is responsible for the design development governance and operational excellence of enterprise data platforms pipelines and analytics capabilities. This role provides technical leadership for data engineering initiatives while managing a team of engineers responsible for delivering scalable secure and reliable data solutions that support business intelligence AI machine learning operational reporting and digital transformation programs.

The successful candidate combines deep technical expertise with leadership skills to establish enterprise data standards drive modernization initiatives implement cloud-native data architectures and ensure data is treated as a strategic business asset. This individual will partner closely with product management application engineering cloud operations architecture security and business leaders to deliver measurable business outcomes.

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

• Bachelors degree in Computer Science Information Systems Engineering Data Science or related field.
• Masters 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.

Leadership Competencies

• Strategic Thinking
• Technical Leadership
• Decision Making
• Stakeholder Management
• Talent Development
• Executive Communication
• Continuous Improvement Mindset
• Customer Focus
• Results Orientation
• Cross-Functional Collaboration

At DXC Technology we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing productivity individual work styles and life circumstances. Were committed to fostering an inclusive environment where everyone can thrive.

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