The
Data Engineering Architect will lead the endtoend architecture, design, and technical execution of data modernization initiatives across cloud, application, and data platforms. This role is responsible for defining scalable data architectures, guiding engineering teams, and ensuring successful migration, integration, and modernization of enterprise data ecosystems.
Key Responsibilities
Architecture & Technical Leadership
- Define the target-state data architecture spanning ingestion, transformation, storage, and consumption layers across cloud platforms.
- Lead the modernization of data pipelines, data platform components, and applicationdata integration patterns.
- Provide architectural guidance for cloudnative services, data engineering frameworks, and analytics/AI readiness.
- Establish best practices for data modeling, schema design, metadata, governance, and lineage.
Data Engineering & Migration Leadership
- Oversee large-scale data migration, ETL/ELT modernization, and pipeline reengineering efforts.
- Direct design and optimization of ingestion frameworks, workflow orchestration, dependency management, and distributed compute architecture.
- Ensure data reliability, performance, and SLAs through technical assessments and optimization strategies.
- Evaluate and modernize legacy data systems, frameworks, and integrations.
Cloud & Platform Alignment
- Work with cloud architects to align data architecture with infrastructure standards, security policies, RBAC, and governance frameworks.
- Drive adoption of cloud-native services such as storage, compute, serverless, AI search, logging, and monitoring.
Collaboration & CrossTeam Alignment
- Partner with application architects, cloud teams, and business/analytics stakeholders to ensure seamless endtoend data flows.
- Work closely with SMEs to validate business rules, ingestion requirements, and domain-specific models.
- Provide technical direction to engineering teams, ensuring consistency with architectural principles.
Quality, Governance & Security
- Ensure adherence to data governance, quality, compliance, and privacy requirements (including PII/PHI constraints).
- Define standards for data lifecycle management, observability, and operational excellence.
- Review and validate requirements, test strategies, and implementation plans.
Documentation & Communication
- Produce architectural diagrams, technical designs, migration plans, and data flow documentation.
- Communicate complex technical concepts to engineering teams, architects, and business stakeholders.
- Support UAT, production readiness, and handover activities during deployment phases.
Required Experience
- Strong background in data engineering, cloud-native data services, ETL/ELT frameworks, and distributed data platforms.
- Extensive experience designing and modernizing data architectures on cloud environments (Azure/AWS/GCP).
- Proficiency with modern data stacks: Spark, Synapse/Databricks, Kafka/Event streams, SQL/NoSQL, Lakehouse platforms.
- Understanding of applicationdata architectures, integration patterns, and DevOps/CI-CD processes.
- Ability to lead technical teams, troubleshoot complex data problems, and drive best practices across engineering functions.
