Key skills
HadoopHDFSPostgresPythonSQLAIMLLLMRAGVector DatabaseQdrantMilvusData EngineeringData WarehousingAnalyticsPostgreSQLGitHubPerformance OptimizationStakeholder ManagementRisk ManagementBudgetingCommunicationSales
About this role
Role Overview
- Design and implement scalable data warehouse and lakehouse architectures on the Cloudera platform
- Define enterprise data models, governance frameworks, data stewardship processes, security standards, and data quality practices
- Architect and optimize analytics solutions across SQL engines including Impala, Hive, and Iceberg
- Design AI-powered analytics solutions leveraging LLMs, Retrieval-Augmented Generation (RAG), vector databases (such as PostgreSQL, Qdrant, Milvus), and NLQ capabilities
- Lead the integration of AI/ML capabilities into enterprise data platforms and data pipelines while establishing governance controls for AI models, data usage, and lifecycle management
- Leverage vibe coding / AI-assisted development tools to accelerate development and improve productivity
- Build and optimize batch and near real-time data pipelines
- Collaborate with business stakeholders to translate business requirements into scalable data products and analytics solutions
- Establish best practices for performance optimization, data architecture, and AI-assisted development
- Mentor teams on modern data architecture and AI-enabled development methodologies
- Ensure data security, governance, compliance, and responsible AI practices within enterprise data platforms and AI-enabled solutions
- Collaborate with business stakeholders across FP&A, Sales, and Revenue Operations to translate business requirements into scalable data solutions that support financial forecasting, revenue optimization, budgeting, pipeline analysis, and sales forecasting
Requirements
- Bachelor’s degree in Computer Science or equivalent and 5-6 years of related experience; OR Master’s degree and 3-5 years of related experience; OR PhD and 0-3 years of related experience
- Deep expertise in enterprise data warehousing, lakehouse architectures, and Cloudera-based data platforms
- Strong experience with CDP, including HDFS, Hive, Impala, Kudu, and Cloudera data ingestion and processing frameworks
- Strong understanding of distributed data systems and Hadoop-based architectures
- Advanced SQL skills, including performance tuning and query optimization
- Proficiency in Python and data engineering frameworks
- Experience with dimensional and normalized data modeling
- Strong understanding of data governance, lineage, metadata management, data cataloging, enterprise security, and compliance requirements
- Experience implementing AI governance practices including model governance, AI risk management, explainability, monitoring, and responsible AI controls
- Experience implementing AI/ML, LLM, vector database, and RAG-based solutions in production environments
- Familiarity with AI-assisted development tools (e.g., GitHub Copilot and LLM-powered workflows)
- Strong communication, stakeholder management, and problem-solving skills
- Ability to align enterprise data architecture with business objectives in Finance, Sales, and Revenue Operations
- Ability to bridge traditional data platforms with modern AI capabilities
Tech Stack
- Hadoop
- HDFS
- Postgres
- Python
- SQL
Benefits
- Generous PTO Policy
- Support work life balance with Unplugged Days
- Flexible WFH Policy
- Mental & Physical Wellness programs
- Phone and Internet Reimbursement program
- Access to Continued Career Development
- Comprehensive Benefits and Competitive Packages
- Paid Volunteer Time
- Employee Resource Groups