Data platform Architect

Data Platform Architect Location :

• Plano TX Visa : US Citizens only Position : Contract to Hire Key Job Duties Architecture Design & Strategy Design and develop end-to-end data architecture to support analytics, reporting, and machine learning initiatives.
• Define the overall data platform strategy, including data ingestion, storage, processing, and access layers.
• Select and implement appropriate data storage solutions (e.g., data lakes, data warehouses, or lakehouses).
• Create reference architecture blueprints aligning with enterprise standards and business goals.   Data Infrastructure & Engineering Build and maintain scalable, high-performance data pipelines using technologies such as Apache Spark, Databricks, Kafka, or AWS Glue.
• Integrate data from multiple internal and external sources into a unified platform.
• Optimize data ingestion, transformation, and ETL / ELT processes for reliability and performance.
• Implement data partitioning, indexing, and caching strategies for faster query performance.   Data Governance, Security & Compliance Define and enforce data governance standards, including metadata management and data lineage tracking.
• Ensure compliance with data privacy regulations (GDPR, HIPAA, etc.).
• Implement robust data security and access control mechanisms across environments.
• Collaborate with InfoSec and compliance teams to ensure proper audit trails and access management.  Cloud & Platform Management Design and manage cloud-based data platforms (e.g., AWS, Azure, GCP).
• Configure cloud data services such as Amazon Redshift, Snowflake, BigQuery, or Azure Synapse Analytics.
• Manage data storage costs and performance through monitoring and optimization.
• Collaboration & Leadership Partner with data engineers, analysts, and business stakeholders to translate business needs into data solutions.
• Provide technical leadership and mentorship to engineering teams.
• Lead data architecture reviews, documentation, and best practice enforcement.
• Communicate architectural decisions and trade-offs to both technical and non-technical audiences.   Performance & Scalability Continuously monitor system performance and optimize data pipelines for scalability.
• Implement data quality frameworks to ensure accurate and consistent data.
• Evaluate new technologies and tools for improving data processing efficiency.

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