> Markdown version of [/jobs/ext/3853646-data-engineering-lead-insurance](https://www.wearedevelopers.com/jobs/ext/3853646-data-engineering-lead-insurance). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineering Lead - Insurance - **Company:** DXC Technology - **Location:** Detroit, MI, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Application Frameworks, Microsoft Azure, Cloud Computing, Cyber Security, Information Systems, Continuous Integration, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Warehousing, Information Lifecycle Management, Python (Programming Language), Machine Learning, Meta-Data Management, Reliability Engineering, Cloud Services, DataOps, Scala (Programming Language), SQL Databases, Enterprise Data Management, Google Cloud, Feature Engineering, Snowflake, Apache Spark, Generative AI, Data Strategy, Event Driven Architecture, Data Lakes, Information Technology, Data Management, Api Design, Stream Processing, Azure Synapse Analytics, Data Pipelines, Amazon Redshift, Databricks - **Published:** October 21, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=0f3b6969a35e42a5 ## About the Role * High school diploma or GED (required). * Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science (Preferred). * Master's degree preferred., * 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 ## Description 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., 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., * 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., * 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., * 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.