Data Pipeline Development
- Design, build, and maintain ETL/ELT pipelines in Databricks to ingest, clean, and transform data from diverse product sources.
- Construct gold layer tables in the Lakehouse architecture that serve both machine learning model training and real-time APIs.
- Monitor data quality, lineage, and reliability using Databricks best practices.
AI-Driven Data Access Enablement
- Collaborate with AI/ML teams to ensure data is modeled and structured to support natural language prompts and semantic retrieval using 1st and 3rd party data sources, vector search and Unity Catalog metadata.
- Help build data interfaces and agent tools to interact with structured data and AI agents to retrieve and analyze customer data with role-based permissions.
API & Serverless Backend Integration
- Work with backend engineers to design and implement serverless APIs (e.g., via AWS Lambda with TypeScript) that expose gold tables to frontend applications.
- Ensure APIs are performant, scalable, and designed with data security and compliance in mind.
- Utilize Databricks and other APIs to implement provisioning, deployment, security and monitoring frameworks for scaling up data pipelines, AI endpoints, and security models for multi-tenancy.
- 3+ years of experience as a Data Engineer or related role in an agile, distributed team environment with a quantifiable impact on business or technology outcomes.
- Proven expertise with Databricks , including job and workflow orchestration, change data capture and medallion architecture.
- Proficiency in Spark or Scala for data wrangling and transformation on a wide variety of data sources and structures.
- Practitioner of CI/CD best practices , test-driven development and familiarity with the MLOps / AIOps lifecycles.
- Proven ability to work in an agile environment with product managers, front-end engineers, and data scientists.
Preferred Skills
- Familiarity with AWS Lambda (Node.js/TypeScript preferred) and API Gateway or equivalent serverless platforms, knowledge of API design principles and working with RESTful or GraphQL endpoints.
- Exposure to React-based frontend architecture and the implications of backend data delivery on UI/UX performance – including end-to-end telemetry to measure performance and accuracy for the end-user experience.
- Experience with A/B testing, experiment and inference logging and analytics.
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