Data Platform Architect Manager

Job description

Snowflake Architect – Manager (10–15 Years)

Our Analytics & Insights Managed Services team brings a unique combination of industry expertise, technology, data management and managed‑services experience to create sustained outcomes for our clients and improve business performance.

We empower companies to transform their approach to analytics and insights while building your skills in exciting new directions.

Have a voice at our table to help design, build, and operate the next generation of data and analytics solutions as an Associate.

Job Requirements and Preferences

Basic Qualifications

• Minimum Degree Required:
• Bachelor’s Degree in Statistics, Mathematics, Computer Science, Data Science, Economics, or a related quantitative field
• Minimum Years of Experience:
• 10–15 years of professional experience in analytics, data science, or business intelligence roles

Preferred Qualifications

• Degree Preferred:
• Master’s Degree in Engineering, Statistics, Data Science, Business Analytics, Economics, or related discipline
• Preferred Fields of Study:
• Data Analytics/Science, Statistics, Management Information Systems, Economics, Computer Science

Preferred Knowledge & Skills

As a Manager and Snowflake Architect, you’ll lead the design, implementation, and optimization of enterprise-scale data platforms on Snowflake.

You will architect data solutions leveraging Snowflake, Python/PySpark, and SQL, enabling scalable analytics and data-driven decision-making.

Collaborating with cloud engineers, data scientists, and business stakeholders, you’ll ensure high-performing, secure, and reliable data solutions across AWS, Azure, or GCP.

In addition to hands-on architecture, you will provide leadership to delivery teams, drive best practices, and mentor associates in building next-generation cloud data platforms.

Snowflake Data Architecture & Engineering

• Lead design and implementation of Snowflake-based enterprise data platforms and analytics solutions
• Architect data models, warehouses, data marts, and secure data sharing patterns in Snowflake
• Optimize performance through clustering, partitioning, query tuning, and workload management
• Define data governance, access control, and compliance policies in Snowflake environments

Programming & Data Engineering

• Develop scalable data processing pipelines using Python and PySpark
• Implement ETL/ELT frameworks integrating structured, semi-structured, and unstructured data
• Leverage advanced SQL for data transformations, aggregations, and analytics workflows
• Standardize reusable components and frameworks for data ingestion and transformation

Cloud Platforms & Integration

• Architect and deploy data solutions on cloud platforms (AWS, Azure, GCP) with Snowflake as the core engine
• Integrate Snowflake with cloud-native services (storage, security, compute, and orchestration)
• Automate provisioning and configuration of data services using cloud-native or IaC tools
• Ensure scalable, resilient, and cost-optimized architecture across multi-environment deployments

Streaming & Real-Time Data (Nice-to-Have)

• Design and implement real-time or streaming data pipelines (Kafka, Kinesis, Event Hubs, Pub/Sub)
• Integrate Snowflake with streaming platforms for low-latency ingestion and analytics
• Define patterns for hybrid batch + streaming workloads

Security, Compliance & Monitoring

• Ensure end-to-end data security with encryption, access policies, and secret management
• Implement role-based access controls, audit trails, and data compliance practices (GDPR, HIPAA, SOX)
• Establish monitoring and alerting frameworks for data pipelines and Snowflake workloads
• Partner with governance teams to ensure enterprise standards and audit readiness

Leadership & Collaboration

• Act as a technical leader guiding architects, engineers, and analysts in Snowflake best practices
• Collaborate with product owners, business stakeholders, and data science teams to deliver solutions
• Lead sprint planning, architecture reviews, and backlog refinement for data platform initiatives
• Drive adoption of modern data engineering practices across agile squads

Documentation & Knowledge Sharing

• Maintain architecture blueprints, technical documentation, and data flow diagrams
• Create reusable playbooks, design patterns, and best practice guidelines for Snowflake adoption
• Conduct knowledge-sharing sessions, peer reviews, and upskilling workshops for delivery teams

Soft Skills & Managerial Readiness

• Strong problem-solving and analytical mindset with focus on scalability, cost efficiency, and reliability
• Ability to communicate data architecture concepts to both technical and business stakeholders
• Proven leadership skills with experience in mentoring, coaching, and team management
• Ownership-driven mindset ensuring quality delivery, innovation, and stakeholder satisfaction

Required Skill Profession

Computer Occupations