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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and building scalable data pipelines using Python, Apache Spark, and Databricks, while ensuring data quality and operational controls. Proficient in modernizing legacy data processing workloads and implementing CI/CD practices for efficient data engineering.
Highest-signal resume keywords
Python DevelopmentApache SparkDatabricksETL/ELT Pipeline DevelopmentData Quality Management
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringSQLData ModellingREST API IntegrationAutomated TestingData Pipeline OptimizationDocument ProcessingStreaming Data PipelinesData IngestionData Quality Best Practices
Soft Skills
Problem-SolvingCommunicationCollaboration
Tools & Technologies
GitCI/CDAzure Cloud ServicesAI-Assisted Development Tools
Industry Keywords
Cloud-Native PlatformsData PipelinesBatch Data IngestionUnstructured DataLegacy Data Processing
Tech Stack
Tools & technologiesApacheAzureCloudETLPySparkPythonSparkSQL
About the role
Key responsibilities & impact- Design, build, and support scalable data pipelines using Python, Apache Spark, PySpark, and Databricks
- Modernize and migrate legacy data processing workloads to secure, cloud-native platforms
- Build and maintain batch data ingestion pipelines from structured and unstructured sources
- Integrate data from REST APIs, SharePoint, document repositories, enterprise applications, and cloud platforms
- Implement data quality, monitoring, observability, and operational controls
- Optimize data workloads for performance, scalability, reliability, and cost efficiency
- Develop document extraction, classification, metadata enrichment, and automation pipelines
- Apply software engineering practices including Git, CI/CD, automated testing, and code reviews
- Build, deploy, troubleshoot, and maintain production ETL/ELT pipelines
- Collaborate with architects, developers, tax subject matter experts, and platform teams
- Analyze existing codebases and identify opportunities to improve maintainability, security, and reliability
- Use AI-assisted development tools to build, review, test, and maintain data pipeline code
Requirements
What you’ll need- 3+ years of experience in data engineering
- Strong Python development experience
- Strong Apache Spark and PySpark experience
- Hands-on experience with Databricks
- Strong SQL and data modelling skills
- Experience building, testing, deploying, and troubleshooting production ETL/ELT pipelines
- Experience with Git-based source control and CI/CD pipelines
- Experience consuming REST APIs for data ingestion, including authentication, pagination, and failure handling
- Experience writing automated tests for data pipelines
- Strong understanding of data quality, reliability, and data engineering best practices
- Strong problem-solving, communication, and collaboration skills
- Experience with Azure cloud services and modernising legacy data pipelines
- Experience with document processing, intelligent document extraction, or unstructured data
- Familiarity with AI-assisted development tools such as Codex, GitHub Copilot, or similar
- Experience with streaming data pipelines or large-scale data repositories
- Ability to analyse unfamiliar codebases, identify gaps and trade-offs, and propose practical improvements
Benefits
Comp & perks- A stable, long-term contract with opportunities for career growth
- Private health insurance
- A remote-friendly culture that promotes work-life balance
- Continuous training, mentorship, and learning programs
- Free access to AI training resources and state-of-the-art AI tools
- Flexible Paid Time Off (PTO) policy and paid holiday days
- Challenging, world-class software projects for clients in the US and LatAm
- Collaboration with talented software engineers in Latin America and the US
- Diverse work environment