Our client is a growing technology company developing market intelligence and software solutions. They are looking for a Senior Data Engineer to join a small international team and take ownership of complex data engineering challenges involving large-scale data processing, standardization, matching, and data quality.
About the role
The role is highly hands-on and suits an engineer who is comfortable working independently, making technical decisions, and taking solutions from initial design through reliable production delivery.
What you’ll do
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Design, build, and maintain scalable production data pipelines.
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Ingest, process, and transform large volumes of data from multiple sources.
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Develop solutions for data standardization, normalization, matching, and validation.
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Build data quality controls and monitoring to identify malformed, inconsistent, or incorrect data.
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Design reliable approaches to data corrections, updates, reprocessing, and backfills.
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Improve the architecture, scalability, reliability, and performance of the data platform.
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Take end-to-end ownership of technical solutions and production quality.
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Work closely with a small engineering team while independently driving your area of responsibility.
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Use AI-assisted engineering tools and practices to improve development efficiency.
What we’re looking for
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4+ years of hands-on Data Engineering experience with production data systems.
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Strong Python skills.
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Hands-on experience with PySpark / Apache Spark and distributed data processing.
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Strong experience building and maintaining ETL/ELT and data ingestion pipelines.
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Experience working with large, complex datasets and multiple data sources.
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Strong SQL and data modeling skills.
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Experience with modern data platforms, data lakes, lakehouse, or similar architectures.
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Strong understanding of data quality, validation, monitoring, and data reliability.
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Ability to independently design solutions, troubleshoot production problems, and take ownership of delivery.
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Fluent English.
Nice to have
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Delta Lake experience.
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MongoDB experience.
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Databricks experience.
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Experience with data matching, reconciliation, deduplication, or complex standardization problems.
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Experience in a startup, scale-up, small product company, or lean engineering team.
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Docker, Linux, CI/CD, or related DevOps experience.
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Experience mentoring or technically supporting other engineers while remaining hands-on.
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Active use of AI coding tools or agent-based development practices.
What we offer
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100% remote work from LATAM.
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Full-time B2B contract.
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High level of technical ownership and autonomy.
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Direct impact on architecture and product development.
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Complex engineering challenges rather than narrowly defined implementation tasks.
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Small, international team with direct communication and minimal bureaucracy.
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Professional development and continuous learning opportunities.