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Open nowPosted 19 days ago

Data Engineer

Workable (global search)108,016 open roles

Where
Argentina
Work mode
Remote
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Your applicationOpen nowData EngineerWorkable (global search) · Argentina

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The clock on this job

Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 7 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 19 days ago

Workable (global search) median: 7 days open

The posting

Data Engineer – Remote

Position Type: Full-Time, Remote Working Hours: U.S. Client Business Hours (flexibility for pipeline monitoring, deployments, and data refresh cycles)

About the Role

At Pavago, one of our clients is hiring a Data Engineer to design, build, and maintain scalable data infrastructure that powers analytics, reporting, and business decision-making.

You’ll build reliable data pipelines, optimize data warehouses, ensure data quality, and collaborate with engineering, analytics, and business teams to deliver trusted, high-performance data solutions.

What You’ll Do

Data Pipelines & Integration

  • Build and maintain ETL/ELT pipelines using Python, SQL, or Scala.
  • Orchestrate workflows with Airflow, Prefect, Dagster, or similar tools.
  • Integrate data from APIs, databases, SaaS platforms, files, and streaming sources.
  • Develop scalable data ingestion workflows.

Data Warehousing & Modeling

  • Manage cloud data warehouses such as Snowflake, BigQuery, or Redshift.
  • Design scalable data models and schemas.
  • Optimize warehouse performance through partitioning, clustering, and indexing.
  • Build analytics-ready datasets for reporting and BI.

Data Quality & Governance

  • Implement validation, monitoring, and anomaly detection.
  • Maintain documentation, lineage, and data governance standards.
  • Ensure reliable, audit-ready data processes.
  • Monitor pipeline health and resolve failures proactively.

Streaming & Real-Time Processing

  • Build and maintain real-time data pipelines.
  • Support event-driven architectures and streaming platforms.
  • Optimize performance and reliability of streaming workflows.

Collaboration & Analytics

  • Partner with analysts, data scientists, and business teams.
  • Support reporting initiatives in Power BI, Tableau, Looker, or similar platforms.
  • Translate business requirements into scalable data solutions.
  • Document pipelines, workflows, and data models.

Infrastructure & Automation

  • Deploy data services using Docker and Kubernetes.
  • Support CI/CD pipelines and cloud infrastructure.
  • Improve system scalability, reliability, and cost efficiency.

Required Experience & Skills

  • 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 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.
  • AWS Glue, GCP Dataflow, or Azure Data Factory.
  • Docker, Kubernetes, Terraform, or CI/CD pipelines.
  • Experience in healthcare, fintech, SaaS, or other regulated industries.
  • Experience optimizing warehouse performance and cloud costs.

What a Typical Day Looks Like

  • Monitor pipeline health and troubleshoot failures.
  • Build and maintain data ingestion pipelines.
  • Optimize SQL queries and warehouse performance.
  • Deliver reliable datasets for analytics and reporting.
  • Implement monitoring and data quality checks.
  • Document pipelines and data models.

In short: You’ll build and maintain reliable data infrastructure that enables accurate reporting, analytics, and business decisions.

Key Metrics for Success

  • 99%+ pipeline uptime.
  • Data freshness maintained within SLA targets.
  • High data quality with minimal downstream issues.
  • Improved warehouse performance and cost optimization.
  • Reliable delivery of scalable datasets.
  • Strong stakeholder satisfaction.

Interview Process

  1. Application Review
  2. Spark Hire Intro Video (3–5 minutes)
  3. Technical Assessment (ETL Pipeline or SQL Exercise)
  4. Client Interview
  5. Offer & Onboarding

What Happens After You Apply

After submitting your application, you’ll receive an email invitation from Spark Hire to record a short 3–5 minute Intro Video. This is the first step in our hiring process and can be completed whenever it’s convenient for you.

Instead of multiple screening calls, you’ll have one opportunity to introduce yourself, discuss your data engineering experience, and highlight your background in data pipelines, cloud platforms, SQL, Python, and data warehousing. Your video will be reviewed before moving forward in the interview process.

You can record your video as many times as you’d like before submitting it—only your final version will be reviewed.

Please keep an eye on both your inbox and spam folder for your Spark Hire invitation after applying.

Apply Now

If you enjoy building scalable data pipelines, optimizing cloud data platforms, and delivering reliable data that drives business decisions, we’d love to hear from you. Apply today and help power the data infrastructure behind a growing organization.

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