Job description

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. Typical Tools & Technologies Languages : • SQL, Python, Scala Data Processing : Apache Spark, Databricks, Airflow Databases : Snowflake, Redshift, BigQuery, Azure Synapse, PostgreSQL Streaming : Kafka, Kinesis Cloud Platforms : AWS, Azure, GCP Data Modeling Tools : ER / Studio, dbt, Power BI Dataflows Powered by JazzHR

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