Senior Databricks Engineer ID86295

AgileEngine, LLC.; Salvador; Híbrido; BRL 180 000 - 290 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Professional growth

Competitive compensation (USD)

Exciting projects with top-tier brands

Flextime

Resumo da oferta

AgileEngine is seeking a Senior Data Engineer to design and operate batch and streaming pipelines on Databricks, PySpark, and Delta Lake. You will migrate legacy ETL workloads to a governed Lakehouse and model a medallion architecture to power analytics and AI use cases.

You will also work with Unity Catalog governance, write high-quality Python/SQL, and optimize Spark performance while collaborating with DevOps and analytics teams.

Qualificações

• 3+ years of professional experience in data engineering with strong Spark experience.
• Hands-on data pipelines with Databricks, PySpark, and Delta Lake.
• Advanced SQL and Python skills with data modeling across dimensional and Lakehouse patterns.
• Experience with Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• Experience migrating legacy ETL/data warehouse workloads to Lakehouse with validated data parity.

Responsabilidades

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion architecture serving analytics, reporting, and ML consumers.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with minimal business disruption.
• Use Claude or GitHub Copilot to accelerate development, generate scaffolding, and write tests.
• Write clean Python and SQL with strong code reviews and documentation.
• Optimize Spark jobs and Delta tables for performance and cost through partitioning, clustering, caching, and sizing.
• Implement data quality, lineage, and governance using Unity Catalog and automated validation checks.
• Debug, troubleshoot, and resolve pipeline failures and production incidents.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance.

Descrição da oferta de emprego

AgileEngine 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 a Senior Data Engineer to build batch and streaming pipelines on Databricks using PySpark and Delta Lake. This person migrates legacy data warehouse and ETL workloads onto a governed Lakehouse, modeling a medallion architecture that powers analytics and AI use cases. Strong SQL, Python, and experience with Unity Catalog governance round out the role.

WHAT YOU WILL DO

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion (bronze/silver/gold) architecture serving analytics, reporting, and machine learning consumers.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with validated data parity and minimal business disruption.
• Use Claude or GitHub Copilot as a development accelerator, generating code scaffolding, writing and reviewing tests, creating documentation, and prototyping solutions.
• Write clean, well-tested Python and SQL; maintain high standards through code review and documentation.
• Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing.
• Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks.
• Debug, troubleshoot, and resolve pipeline failures, data defects, and production incidents.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

MUST HAVES

• 3+ years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.
• Strong hands-on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.
• Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.
• Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.
• Strong problem-solving, collaboration, and communication skills.
• Familiarity with Unity Catalog, data governance, access control, and PII handling.
• Experience with dbt or an equivalent transformation framework.
• Familiarity with secure coding standards and industry security best practices.

NICE TO HAVES

• Experience with Infrastructure as Code (IaC) using Terraform and CI/CD using Azure DevOps.
• Experience working with relational databases (specifically PostgreSQL) and data persistence concepts.
• Familiarity with logging and monitoring tools (e.g., Dynatrace, CloudWatch, Databricks system tables).
• Experience working in Agile or team-based development environments preferred.

PERKS AND BENEFITS

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• Competitive 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.