Data Engineer Id89384

Agileengine; Manaus; Hybrid; BRL 150,000 - 210,000; Full time

Benefits offered by this job

Mentorship

Tech Talks

Education budget

Flexible schedule

Job summary

Agile Engine 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. You will design, develop, and optimize data pipelines using PySpark and SQL, integrate with BigQuery and Delta Lake, and implement data quality checks and production controls to ensure reliability at scale.

Join a people-first culture delivering data solutions for Fortune 500 brands and startups across

Qualifications

• Strong hands-on experience with Databricks and PySpark.
• Advanced SQL and data-processing skills, with GCP/BIGQuery proficiency.
• Experience with Delta Lake and modern data lake/lakehouse architectures.
• Expertise in ETL/ELT, data pipeline design, performance optimization, and data quality.
• Proven track record building reliable, scalable data solutions and production-grade systems.
• Solid analytical, troubleshooting skills and knowledge of software engineering practices.
• Upper-intermediate English level.

Responsibilities

• Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, PySpark, and SQL.
• Build and operationalize agentic workflows to automate data engineering and related processes.
• Integrate agentic capabilities with Databricks, GCP, BigQuery, and Delta Lake environments.
• Develop data pipelines to support new business requirements and datasets.
• Create reusable frameworks and components for multiple use cases.
• Implement data quality checks, monitoring, and production controls.
• Optimize PySpark and SQL workloads for performance and reliability.
• Support testing, deployment, productionization, and ongoing enhancements.
• Collaborate with business, data engineering, and platform teams to identify further automation opportunities.

Skills

Databricks

PySpark

SQL

GCP

Tools

BigQuery

Delta Lake

Data Lakehouse

Job description

Job Description

Agile Engine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for aData Engineerwith strong Databricks and GCP experience to develop scalable ETL/ELT pipelines and productionize agentic workflows for data engineering automation.

WHAT YOU WILL DO

Design, develop, and optimize scalable ETL/ELT data pipelines using Databricks, Py Spark, 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, Big Query, and Delta Lake environments.

Develop data pipelines and processing solutions to support new business requirements and datasets.

Build reusable frameworks and components that can be leveraged across multiple data engineering and business use cases.

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

Optimize Py Spark and SQL workloads for performance, reliability, and scalability.

Support testing, deployment, productionization, and ongoing enhancement of data and agentic solutions.

Troubleshoot complex data and production issues and implement sustainable llaborate with business, data engineering, and platform teams to identify further automation opportunities.

MUST HAVES

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

Advanced SQL and data-processing skills. Hands-on experience withGCP, particularly Big Query.

Experience withDelta 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.

PERKS AND BENEFITS

Professional growth : Accelerate your professional journey with mentorship, Tech Talks, and personalized growth petitive compensation : We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities. A selection of exciting projects : Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.

Flextime : Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.

Requirements

Strong hands-on experience with Databricks and Py Spark. Advanced SQL and data-processing skills. Hands-on experience with Google Cloud Platform (GCP), particularly Big Query. 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.