Data Engineer ID89384

We are looking for a Data Engineer with strong Databricks and GCP experience to build scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.

The mandatory requirements are 5+ years of hands-on Databricks and PySpark experience, advanced SQL skills, strong GCP and BigQuery knowledge, and experience with Delta Lake and lakehouse architectures.

1. Application — Share a few details about your experience and background.

2. Coding Challenge — If applicable, complete it in the coding language you are most comfortable with.

3. Video Interview — Record a short video introduction in English.

4. Technical Interview or Hiring Manager Interview — Discuss your experience and fit for the role.

5. Offer — If it’s a match, you’ll receive an offer.

- Remote work — Work from where you feel most productive.

- Local presence in India — Work in a structured and compliant environment aligned with Indian regulations.

- Competitive compensation in INR — Receive compensation in INR, plus support for learning, education, and wellness.

- Exciting projects — Work with modern technologies for global clients and fast-growing companies.

- 5+ years of strong hands-on experience with Databricks and PySpark.

- Advanced SQL and data-processing skills.

- Hands-on experience with GCP, particularly BigQuery.

- Experience with Delta Lake and modern data lake/lakehouse architectures.

- Strong understanding of ETL/ELT, data pipeline design, performance optimization, and data quality.

- Experience building reliable, scalable, production-grade data solutions.

- Strong analytical and troubleshooting skills.

- Understanding of software engineering practices, including testing, version control, deployment, monitoring, and production support.

- Upper-intermediate English level.

- Experience developing or integrating AI/agentic workflows, AI agents, or workflow automation solutions.

- Experience applying AI to automate data engineering, validation, troubleshooting, or operational processes.

- Familiarity with orchestration and automation frameworks.

- Experience developing reusable data engineering frameworks and platform components.

- Exposure to productionizing AI-enabled solutions with appropriate validation, monitoring, and human oversight.

- Design, develop, and optimize scalable ETL/ELT pipelines using Databricks, PySpark, and SQL.

- Build and operationalize agentic workflows that automate data validation, issue identification, troubleshooting, and workflow execution.

- Integrate agentic capabilities with Databricks, GCP, BigQuery, and Delta Lake environments while supporting new business requirements and datasets.

- Build reusable frameworks and components that can support multiple data engineering and business use cases.

- Implement data quality checks, monitoring, validation, exception handling, and production controls.

- Optimize PySpark and SQL workloads and support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.

- Troubleshoot complex production issues and collaborate with business, data engineering, and platform teams to identify additional automation opportunities.