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Data Platform Architect
2 weeks ago
The lead will oversee the development and scaling of data and AI capabilities across the organization, utilizing Databricks Unified Analytics Platform. This role requires technical vision, solution architecture, team building, partnership development, and delivery excellence.
Key Responsibilities:
- Offering and Capability Development: Develop and enhance Snowflake-based data platform offerings and accelerators, define best practices, architectural standards, and reusable frameworks for Snowflake, and collaborate with alliance teams to strengthen partnership with Snowflake.
- Technical Leadership: Provide architectural guidance for Snowflake solution design and implementation, lead solutioning efforts for proposals, RFIs, and RFPs involving Snowflake, conduct technical reviews, and ensure adherence to design standards.
- Delivery Oversight: Support delivery teams with technical expertise across Snowflake projects, drive quality assurance, performance optimization, and project risk mitigation, review project artifacts, and ensure alignment with Snowflake best practices.
- Talent Development: Build and grow a high-performing Snowflake capability team, define skill development pathways and certification goals for team members, mentor architects, developers, and consultants on Snowflake technologies, and drive community of practice initiatives to share knowledge and innovations.
- Business Development Support: Engage with sales and pre-sales teams to position Snowflake capabilities, contribute to account growth by identifying new Snowflake opportunities, participate in client presentations, workshops, and technical discussions.
- Thought Leadership and Innovation: Build thought leadership through whitepapers, blogs, and webinars, stay updated with Snowflake product enhancements and industry trends.
This role is highly collaborative and will work closely with cross-functional teams to fulfill the above responsibilities.
Requirements:
- 12–15 years of experience in data engineering, analytics, and AI/ML
- 3–5 years of strong hands-on experience with Databricks (on Azure, AWS, or GCP)
- Expertise in Spark (PySpark/Scala), Delta Lake, Unity Catalog, MLflow, and Databricks notebooks
- Experience designing and implementing Lakehouse architectures at scale
- Familiarity with data governance, security, and compliance frameworks (GDPR, HIPAA, etc.)
- Experience with real-time and batch data pipelines (Structured Streaming, Auto Loader, Kafka, etc.)
- Strong understanding of MLOps and AI/ML lifecycle management
- Certifications in Databricks (e.g., Databricks Certified Data Engineer Professional, ML Engineer Associate) are preferred
- Experience with hyperscaler ecosystems (Azure Data Lake, AWS S3, GCP GCS, ADF, Glue, etc.)
- Experience managing large, distributed teams and working with CXO-level stakeholders
- Strong problem-solving, analytical, and decision-making skills
- Excellent verbal, written, and client-facing communication
This position offers a unique opportunity to leverage cutting-edge technologies and drive business outcomes in a dynamic environment.
