Data Engineer

TL;DR

Data Engineer (Python/SQL): Building and maintaining scalable data infrastructure, pipelines, and cloud data warehouses that power analytics, reporting, and business decisions with an accent on data reliability, modeling, orchestration, and real-time processing. Focus on optimizing warehouse performance, monitoring pipeline health, implementing data quality controls, and deploying resilient data services during U.S. client business hours.

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.

Company

hirify.global is sourcing candidates for a client seeking scalable data engineering and infrastructure expertise.

What you will do

• Build and maintain ETL/ELT pipelines using Python, SQL, or Scala and orchestrate workflows with Airflow, Prefect, Dagster, or similar tools.
• Integrate data from APIs, databases, SaaS platforms, files, and streaming sources into scalable ingestion workflows.
• Design data models and schemas, manage cloud data warehouses, and optimize performance through partitioning, clustering, and indexing.
• Implement data validation, monitoring, anomaly detection, documentation, lineage, and governance processes.
• Build real-time data pipelines and support event-driven architectures and streaming platforms.
• Deploy data services with Docker and Kubernetes, support CI/CD and cloud infrastructure, and collaborate with analytics, engineering, data science, and business teams.

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

Culture & Benefits

• Fully remote full-time position.
• Work aligned with U.S. client business hours.
• Opportunity to build data infrastructure supporting analytics and business decision-making.
• Success measured through pipeline uptime, data freshness, data quality, warehouse performance, 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 followed by offer and onboarding.