Data Engineer ID89384

AgileEngine, LLC.; Maharashtra; À distance; INR 1 200 000 - 2 400 000; Plein temps

Avantages offerts par ce poste

Remote work

Local presence in India

Competitive compensation in INR

Exciting projects

Résumé du poste

AgileEngine, LLC. in India is seeking a Data Engineer with strong Databricks and GCP experience to develop scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.

The role requires 5+ years with Databricks and PySpark, advanced SQL, hands-on GCP, and experience with Delta Lake and modern lakehouse architectures. You will design reliable data solutions and optimize performance.

Qualifications

• 5+ years of hands-on Databricks and PySpark experience.
• Advanced SQL and data-processing skills.
• Hands-on GCP experience (BigQuery preferred).
• Experience with Delta Lake and modern lakehouse architectures.
• Strong understanding of ETL/ELT and data pipeline design.

Responsabilités

• Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering processes.
• Integrate agentic capabilities with Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines and reusable frameworks to support new business requirements.
• Implement data quality checks, monitoring, and production controls.
• Optimize PySpark and SQL workloads for performance and reliability.
• Support testing, deployment, and ongoing enhancement of data solutions.

Connaissances

Databricks

PySpark

Advanced SQL

ETL/ELT design

Data quality

Outils

Databricks

BigQuery

Delta Lake

GCP

Description du poste

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

The mandatory requirements are 5+ years of experience with Databricks and PySpark, advanced SQL, hands-on GCP experience, and experience with Delta Lake and modern 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.
• Upper-intermediate English level.

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 data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering and operational processes such as data validation, issue identification, troubleshooting, and workflow execution.
• Integrate agentic capabilities with existing Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines and reusable frameworks and components to support new business requirements and datasets across multiple use cases.
• Implement data quality checks, monitoring, validation, exception handling, and production controls.
• Optimize PySpark and SQL workloads for performance, reliability, and scalability.
• Support testing, deployment, productionization, troubleshooting, and ongoing enhancement of data and agentic solutions.
• Collaborate with business, data engineering, and platform teams to identify further automation opportunities.