DataE2E Technologies
Website:
datae2e.com
Job details:
Data + AI Engineer
Job Type: Full-Time / Permanent
Work Location: 100% Remote
Shift Timing: 3 pm – 11 pm IST
Job Summary
Job Summary
Data engineering
- Design and develop scalable data pipelines using Databricks, Spark, SQL and cloud-native
- technologies
- Build and maintain bronze, silver and gold data products following medallion architecture principles
- Develop robust ELT/ETL frameworks supporting batch and streaming workloads
- Implement data modeling standards, optimization strategies and performance tuning practices
- Ensure engineering solutions meet enterprise standards for scalability, reliability and maintainability
Agentic AI engineering
- Use Speckit and enterprise knowledge repositories to give AI agents contextual grounding, so
- generated code, documentation, tests and engineering artifacts align with organizational standards and
- best practices
- Design knowledge-driven agent workflows that draw on documented business rules, data models, architecture patterns and engineering guidelines
- Build AI-assisted solutions for: Spark and SQL code generation, Data pipeline generation
- Data quality rule generation, Unit test generation, Code review and optimization, CI/CD and deployment automation
- Develop multi-agent workflows that orchestrate end-to-end engineering tasks
- Integrate Large Language Models (LLMs) into enterprise data engineering processes
- Script against REST APIs and SDKs (Power BI/Fabric, Databricks, Jira or Azure DevOps), and use
- Tabular Editor for bulk model changes
- Build evaluation harnesses for AI agents and orchestrate workloads with tools such as Databricks Jobs or Airflow
- Create reusable prompt frameworks, agent instructions, templates and engineering accelerators
- Define guardrails and quality controls for AI-generated code and artifacts
- Implement human-in-the-loop review and governance mechanisms
Experience
- 8+ years in Data Engineering, Analytics Engineering or Data Platform development
- Hands-on experience building agentic AI solutions
- Hands-on experience developing solutions on Databricks
- Experience implementing enterprise-scale data pipelines and data products
- Demonstrated experience with software engineering best practices and DevOps methodologies
- Experience with Git, pull-request review, automated testing and CI/CD pipelines for analytics or data assets
- Experience with natural-language analytics, MCP or spec-driven development; familiarity with responsible AI frameworks and AI cost controls
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