Enterprise Data Architect & AI Solutions Leader
New
A
AnswerRocketArtificial Intelligence
Applicants must reside in the United StatesFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 10+ years of experience in data architecture and engineering, with 5+ years in enterprise-scale systems and 3+ years in AI/ML platforms
- Required Skills
- PythonSQLCloud ComputingTypeScriptDatabricksMLOpsGenerative AI
Requirements
- Bachelor's degree in Computer Science, Data Engineering, or related field (Master's preferred); or equivalent industry experience
- 10+ years of experience in data architecture and engineering
- 5+ years of experience in enterprise-scale systems
- 3+ years of experience in AI/ML platforms
- Hands-on expertise with Databricks, Delta Lake, and Unity Catalog
- Proficiency in TypeScript, Python, and SQL
- Experience with cloud serverless architectures (AWS, GCP, Azure)
- Experience building RAG pipelines, vector databases, LLM operations, and multi-model AI systems
- Strong leadership skills with ability to lead technical teams and influence enterprise stakeholders
- Proven consulting experience with enterprise clients
- Excellent communication skills with ability to present to C-level executives
Responsibilities
- Lead data strategy initiatives including current state assessments, enterprise architecture design, and governance frameworks
- Design and implement cloud-native data lakehouse platforms (Databricks, Snowflake, BigQuery) with medallion architectures
- Build real-time and batch data pipelines using modern ETL/ELT, streaming, and orchestration technologies
- Architect and develop generative AI solutions including RAG pipelines, multi-agent systems, and autonomous monitoring
- Create advanced analytics and BI solutions with modern self-service platforms (Tableau, Power BI)
- Lead technical teams, mentor data professionals, and drive innovation lab initiatives
- Conduct client discovery sessions and translate complex technical concepts for executive audiences
- Implement MLOps, feature stores, and AI/ML pipeline development with performance optimization
- Establish data governance standards, quality monitoring, and observability engineering practices
View Full Description & ApplyYou'll be redirected to the employer's site
