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.

WHAT YOU’LL GAIN

• 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.

MUST HAVES

• 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.

NICE TO HAVES

• 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.

WHAT YOU WILL DO

• 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.