Senior Data Engineer, Databricks

Compass

Data Engineer role focused on evolving Corporate Data Platforms using AWS and Databricks technologies. Join Compass UOL in driving the AI revolution.

Posted 7/14/2026full-timeRemote • 🇧🇷 BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining scalable data pipelines and implementing Data Engineering standards within Cloud environments, particularly AWS. Proficient in modern data architectures including Data Lake, Data Warehouse, and Lakehouse, with a strong focus on data governance and quality.

Highest-signal resume keywords

Advanced SQL ProficiencyExperience Building ETL/ELT PipelinesHands-On Experience With DatabricksKnowledge Of Apache SparkExperience With Cloud Environments, Preferably AWS

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Advanced SQL ProficiencyETL/ELT Pipeline DevelopmentData Pipeline MaintenanceData GovernanceData Quality AssuranceData ModelingData TransformationData IngestionData Delivery SolutionsData Cataloging

Tools & Technologies

DatabricksApache SparkAWS GlueUnity CatalogLake FormationGitAirflowKafkaDbt

Industry Keywords

Data LakeData WarehouseLakehouse ArchitectureDataOpsCI/CD PracticesCloud EnvironmentsData IntegrationLegacy Pipeline RefactoringCorporate Data PlatformsMigration Projects

Tech Stack

Tools & technologies

AirflowApacheAWSAzureCassandraCloudETLKafkaMongoDBNoSQLPostgresSparkSQLUnity

About the role

Key responsibilities & impact

Implement and evolve the Corporate Data Platform (Enterprise Lakehouse);
Participate in modernizing the corporate analytics ecosystem, migrating workloads from Azure to AWS and Databricks;
Develop, maintain and evolve scalable, reliable and high-performance data pipelines;
Build data ingestion, transformation and delivery solutions using Lakehouse architecture;
Implement Data Engineering standards, DataOps, CI/CD and code versioning;
Support data governance, data quality and data cataloging initiatives;
Refactor legacy pipelines into modern solutions based on Apache Spark and Databricks;
Work on complex integrations between corporate systems, ensuring scalability, governance and data reliability;
Collaborate in implementing Data Lake, Data Warehouse and Lakehouse architectures in Cloud environments.

Requirements

What you’ll need

Advanced SQL proficiency;
Experience building ETL/ELT pipelines;
Hands-on experience with Databricks;
Knowledge of Apache Spark;
Experience with Data Lake, Data Warehouse and/or Lakehouse architectures;
Knowledge of analytical and dimensional data modeling;
Experience processing large volumes of data;
Experience with Cloud environments, preferably AWS;
Knowledge of AWS Glue, Unity Catalog and Lake Formation;
Experience with Git and code versioning;
Knowledge of CI/CD practices applied to Data Engineering;
Experience with tools such as Airflow, Kafka and dbt;
Knowledge of SQL and NoSQL databases (PostgreSQL, MongoDB and Cassandra);
Experience in modernization and migration projects for corporate data platforms;
Ability to work on complex integrations between corporate systems and distributed environments.

Benefits

Comp & perks

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