Job Summary
Design and optimize robust data platforms as an Architect with extensive experience in Python Databricks SQL Databricks Workflows and PySpark working in a hybrid model during day shifts and focusing on scalable analytics solutions that power critical business decisions while improving data reliability performance and usability across the enterprise.

Responsibilities
Define and maintain end to end data architecture blueprints that leverage Python Databricks SQL and PySpark to deliver scalable analytics solutions that directly support enterprise decision making and long term data strategy.
Design optimized data models and lakehouse structures on Databricks that improve query performance reduce compute costs and enhance reliability for business intelligence and advanced analytics use cases.
Develop robust data ingestion and transformation pipelines using PySpark and Databricks Workflows to ensure timely accurate and well structured data is available for downstream consumption by analysts and data scientists.
Configure and orchestrate Databricks Workflows to automate complex data processes enforce dependencies and provide predictable repeatable execution patterns for critical data jobs.
Collaborate with product owners and business stakeholders to translate analytical needs into practical technical architectures that align with organizational goals and deliver measurable business value.
Implement strong data governance practices including data quality rules validation checks and monitoring frameworks to increase trust in enterprise data assets and reduce operational risk.
Optimize Python and PySpark code for performance scalability and maintainability by applying best practices in modular design resource management and efficient data handling approaches.
Guide teams on effective use of Databricks SQL for analytics reporting and interactive exploration ensuring queries are tuned and consistent with established data modeling standards.
Establish and document architecture patterns standards and guidelines for hybrid work delivery enabling distributed teams to collaborate effectively while maintaining secure access to data platforms.
Evaluate new features within Databricks and related data ecosystem tools to recommend improvements that enhance platform capabilities usability and alignment with future business needs.
Partner with security and compliance teams to design architectures that protect sensitive data meet regulatory requirements and provide robust access control within the Databricks environment.
Mentor less experienced data engineers and architects by sharing best practices in Python development PySpark optimization and Databricks platform usage to elevate overall team capability.
Monitor production data pipelines and workflows to proactively identify performance bottlenecks capacity issues or failure patterns and implement sustainable technical remediation.

Qualifications
Demonstrate extensive hands on experience designing and implementing data engineering solutions using Python with a strong focus on clean coding practices and reusable components.
Show deep proficiency in Databricks SQL including writing complex analytical queries tuning execution plans and working with large scale structured and semi structured datasets.
Apply advanced PySpark skills to build high volume data pipelines manage distributed processing and optimize transformations for both performance and reliability.
Exhibit strong practical knowledge of Databricks Workflows including orchestration of multistep jobs scheduling strategies and integration with other platform services.
Bring solid understanding of modern data architecture concepts such as lakehouse design batch and near real time processing and metadata management for enterprise scale implementations.
Demonstrate experience working in hybrid work models that require effective remote and onsite collaboration while maintaining secure and efficient access to shared data environments.
Display capacity to communicate complex technical designs in clear business oriented language enabling stakeholders to understand architectural tradeoffs and expected outcomes.