The Role
Leads enterprise data modernization initiatives by designing scalable cloud data architectures, modernizing pipelines and ETL/ELT frameworks, directing migrations, and guiding engineering teams. Establishes standards for modeling, governance, metadata, lineage, security, reliability, observability, and data quality. Partners with application, cloud, analytics, and business stakeholders to align integrations and data flows. Produces technical designs and migration documentation while supporting testing, production readiness, deployment, and handover activities.
Summary Generated by Built In
The Data Engineering Architect will lead the end‑to‑end 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 ResponsibilitiesArchitecture & 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 application‑data integration patterns.
- Provide architectural guidance for cloud‑native services, data engineering frameworks, and analytics/AI readiness.
- Establish best practices for data modeling, schema design, metadata, governance, and lineage.
- Oversee large-scale data migration, ETL/ELT modernization, and pipeline re‑engineering 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.
- 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.
- Partner with application architects, cloud teams, and business/analytics stakeholders to ensure seamless end‑to‑end 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.
- 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.
- 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.
- 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 application‑data architectures, integration patterns, and DevOps/CI-CD processes.
- Ability to lead technical teams, troubleshoot complex data problems, and drive best practices across engineering functions.
Skills Required
- Strong background in data engineering, cloud-native data services, ETL/ELT frameworks, and distributed data platforms
- Extensive experience designing and modernizing data architectures on Azure, AWS, or GCP
- Proficiency with Spark
- Proficiency with Synapse or Databricks
- Proficiency with Kafka or event-streaming platforms
- Experience with SQL and NoSQL databases
- Experience with Lakehouse platforms
- Understanding of application-data architectures and integration patterns
- Understanding of DevOps and CI/CD processes
- Ability to lead technical teams and troubleshoot complex data problems
- Ability to drive best practices across engineering functions
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The Company
What We Do
Anblicks is a Cloud Data Analytics Company based out of Dallas, TX, with offices in USA, India, and Australia. Since 2004, Anblicks has been helping customers by bringing value to their data and implementing modern data architecture and advanced analytics solutions in the cloud.
