Senior Data Engineer – Azure Databricks

Applaudo

. Design, build, and maintain reliable, production-grade data pipelines using Azure Databricks and Python .

Posted 10/22/2026full-timeRemote • Costa RicaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining data pipelines using Azure Databricks and Python, with a strong focus on data warehousing, dimensional modeling, and data quality validation. Proficient in integrating diverse data sources and optimizing SQL transformations for performance.

Highest-signal resume keywords

Azure DatabricksPython Data ProcessingApache Spark (PySpark/Spark SQL)SQL ProficiencyData Warehousing and Dimensional Modeling

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentData TransformationSQL OptimizationData Quality ValidationData IntegrationDimensional ModelingData Mart DevelopmentAPI Data IngestionTroubleshooting Data DiscrepanciesData Processing with PySpark

Soft Skills

Problem-SolvingAnalytical SkillsOrganizational SkillsAutonomyCollaboration

Tools & Technologies

Azure Data ServicesRelational DatabasesCloud StorageSFTPData Hub Integration

Industry Keywords

Data EngineeringData Quality ChecksData Transformation LogicTechnical DocumentationData Pipeline Optimization

Tech Stack

Tools & technologies

ApacheAzureCloudPySparkPythonSparkSQL

About the role

Key responsibilities & impact

Design, build, and maintain reliable, production-grade data pipelines using Azure Databricks and Python
Develop scalable data transformations using PySpark or Spark SQL
Ingest and process data from APIs, relational databases, cloud storage, and SFTP sources
Integrate data from ticketing, CRM, marketing, retail, and other enterprise platforms into a centralized Data Hub
Develop and maintain data warehouse structures, dimensional models, and data marts
Write, optimize, and maintain SQL transformations
Implement data quality validations and consistency checks
Build and maintain outbound data feeds for third-party systems
Investigate data discrepancies and troubleshoot pipeline issues
Document data pipelines, transformation logic, dependencies, and technical processes
Collaborate with team members and stakeholders to clarify requirements and identify risks
Communicate progress, blockers, and potential delays proactively, proposing next steps to keep deliverables on track

Requirements

What you’ll need

5+ years of professional experience in Data Engineering
Strong hands-on experience with Azure Databricks
Advanced Python skills for data processing, transformation, and pipeline development
Solid experience with Apache Spark, including PySpark and/or Spark SQL
Advanced SQL proficiency, including complex transformations, data validation, and query performance optimization
Strong knowledge of data warehousing and dimensional modeling, including facts, dimensions, and data marts
Experience integrating data from REST APIs, relational databases, cloud storage, and SFTP
Hands-on experience with Microsoft Azure data services for data ingestion, storage, processing, and integration
Experience implementing data quality checks and validating consistency between source systems and target data models
Ability to troubleshoot pipeline failures, investigate data discrepancies, and implement reliable solutions
Strong problem-solving, analytical, and organizational skills
Ability to work autonomously, take ownership, and follow established engineering practices and documentation standards
Strong English communication skills and ability to collaborate with a US-based team
Experience migrating SQL-based data workloads to Azure Databricks is nice to have

Benefits

Comp & perks

Remote work option
Opportunity to learn fast and work alongside strong teams
Modern ways of working
Participation in AI-native transformation at scale