Job ID: D6392
To apply, email softee@softinc.com with the subject line " Job ID: D6392 " and attach your resume and any other documents to the email.
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SOFT's client located in New York, NY ( Hybrid ) is looking for a Cloud Data Architect - AWS / Databricks for a long term contract assignment.
Qualifications:
8+ years of experience in data architecture, data engineering or database design and large-scale enterprise cloud data implementations in complex, highly regulated environments with Databricks platform. Deep understanding of modern data technology stacks, cloud data platforms (AWS preferred), and enterprise software solutions.
5+ years of experience in Tableau visualization preferred.
Demonstrated experience designing, architecting and supporting applications with a strong emphasis on scalability, security, availability and performance. Demonstrated knowledge of and leading adoption of industry best practices in the areas of DataOps and modern data stack tools, data governance, SQL and other query tools.
Demonstrated experience in cloud data architecture skills including designing, architecting and delivering modern cloud data platforms to ingest, process, store and expose data across the enterprise. Expert knowledge of data modeling techniques and tools, data lifecycle management and integration patterns to ensure that data flows are consistent, reliable and reusable across services and solutions. Experience with data storage technologies, partitioning strategies, caching, data access patterns, and robust security strategies enforcing privacy and regulatory requirements including designing standardized approaches for data classification, access control, encryption, retention and auditability. Knowledge of data lake houses, data warehousing concepts and data governance.
Outstanding communication and collaboration skills, including translating technical topics and risks into business teams. Ability to personalize communication to audience from technologists to executive leadership.
Extensive, in-depth experience implementing highly complex technology projects in a cross-functional matrix environment with demonstrated ability to drive consensus and deliver results. Examples of leading through influence and successfully resolving conflicting priorities are required.
Bachelor's degree in computer science or similar field with 8+ years of related work experience.
Responsibilities:
Architect scalable data processing frameworks leveraging Databricks platform for analytical workloads
Implement cost-optimization strategies balancing compute resources, storage tiering, and cluster configurations
Develop and maintain coding standards for PySpark/Python within Databricks notebooks ensuring maintainability and performance
Design and architect ETL/ELT pipelines supporting ingestion, transformation, and distribution of analytic datasets.
Design Databricks workspace architectures integrating Unity Catalog for centralized governance, AWS IAM for authentication, and cross-account data access patterns.
Design data quality frameworks and validation logic within processing workflows
Understanding of complex SQL queries and Spark transformations for data manipulation, aggregation, and analytical computations
Establish patterns for incremental processing, change data capture, and historical data management
Architect MLOps pipelines for model deployment and monitoring using AWS services, Domino Data Lab, and related platforms
Implement CI/CD workflows using GitLab for data pipeline deployment and version control
Establish infrastructure-as-code practices for environment consistency and disaster recovery
Establish data governance frameworks within centralized data lake environment
Design dimensional data models and semantic layers optimized for Tableau and BI consumption
Explore and implement generative AI capabilities
Partner with product owners, architects, data engineers, analysts, and application teams to translate requirements into technical architectures
Mentor team members on best practices for Spark optimization, data modeling, and cloud-native design patterns
Evaluate emerging technologies and platforms for potential adoption into the data ecosystem
Document architectural decisions, design patterns, and technical standards