Data Engineer – Remote (Python)

Location: Remote from Brazil, Costa Rica, or Argentina; working hours aligned with U.S. client business hours, with flexibility for pipeline monitoring, deployments, and data refresh cycles.

Requirements

• 3+ years of experience in data engineering, data infrastructure, or back-end engineering.
• Strong Python and SQL skills.
• Experience with Snowflake, BigQuery, Redshift, or similar cloud data warehouses.
• Hands-on experience with Airflow, Prefect, or similar workflow orchestration tools.
• Strong understanding of ETL/ELT pipelines and data modeling.
• Experience with AWS, Azure, or Google Cloud.

Nice to have

• Experience with dbt, Kafka, Kinesis, Pub/Sub, or other streaming platforms.
• Experience with AWS Glue, GCP Dataflow, or Azure Data Factory.
• Knowledge of Docker, Kubernetes, Terraform, or CI/CD pipelines.
• Experience in healthcare, fintech, SaaS, or other regulated industries.
• Experience optimizing warehouse performance and cloud costs.

What you will do

• Build and maintain ETL/ELT pipelines and scalable data ingestion workflows using Python, SQL, or Scala.
• Orchestrate workflows and integrate data from APIs, databases, SaaS platforms, files, and streaming sources.
• Design data models and schemas, manage cloud data warehouses, and optimize partitioning, clustering, indexing, performance, and cost.
• Implement data validation, monitoring, anomaly detection, lineage, documentation, and governance standards.
• Build and support real-time, event-driven data pipelines and resolve pipeline failures proactively.
• Collaborate with analysts, data scientists, engineers, and business teams to deliver reliable datasets for reporting and BI platforms.

Culture & Benefits

• Full-time remote position.
• Work aligned with U.S. client business hours, with flexibility around monitoring and deployment cycles.
• Success is measured by pipeline uptime, SLA-aligned data freshness, data quality, warehouse performance, cost efficiency, and stakeholder satisfaction.

Hiring process

• Application review followed by a 3–5 minute Spark Hire introductory video.
• Technical assessment covering an ETL pipeline or SQL exercise.
• Client interview, offer, and onboarding.