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Middle Data Engineer (Databricks Lakehouse)

AgileEngine Brazil
Remote
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AI Summary

AgileEngine is seeking a Middle Data Engineer to modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will design and build batch and streaming pipelines using PySpark and Delta Lake, implement a medallion (bronze/silver/gold) architecture, and migrate legacy ETL workloads with validated data parity. The role requires strong SQL and Python skills, hands-on experience with Databricks, Structured Streaming, and Unity Catalog, and offers 100% remote work with a collaborative, people-first culture.

Key Highlights
Modernize a 15-year-old legacy data warehouse into a governed Databricks Lakehouse
Build batch and streaming data pipelines with PySpark and Delta Lake following medallion architecture
Migrate legacy ETL workloads with validated data parity and minimal business disruption
Implement data quality, lineage, and governance controls using Unity Catalog
Work 100% remotely with flexible hours and a supportive, zero-micromanagement culture
Key Responsibilities
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
Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks
Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing
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
Technical Skills Required
Apache Spark (PySpark) Delta Lake SQL
Benefits & Perks
100% remote work with flexible hours
Dedicated annual learning budget and internal TechTalks
Competitive compensation with regular performance reviews
Well-being programs and people-focused support
Nice to Have
Experience with Infrastructure as Code (Terraform) and CI/CD using Azure DevOps
Experience with streaming ingestion using Kafka or Event Hubs
Experience with workflow orchestration tools such as Airflow
Familiarity with logging and monitoring tools (e.g., Dynatrace, CloudWatch, Databricks system tables)
Experience with dbt or an equivalent transformation framework
Experience working with relational databases (specifically PostgreSQL) and data persistence concepts
Experience working in Agile or team-based development environments

Job Description


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 Middle Data Engineer to help modernize a 15-year-old data warehouse into a governed Databricks Lakehouse. You will build batch and streaming pipelines with PySpark and Delta Lake, following a medallion architecture across bronze, silver, and gold layers. This role also uses AI tools like Claude and GitHub Copilot to speed up development.

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.
- Experience delivering production data platforms at scale.
- Upper-intermediate English level.

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
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location



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