Data Engineer

🚀 Data Engineer (Python, SQL, ETL, Airflow, Snowflake, BigQuery)

Full-Time | Remote | U.S. Business Hours

💡 About the Role

We’re hiring a highly technical Data Engineer to build and maintain scalable data pipelines, cloud data infrastructure, and analytics-ready datasets that power business decision-making.

This role is focused on:

✅ ETL/ELT pipeline development

✅ Data warehouse architecture

✅ SQL optimization

✅ Cloud-based data infrastructure

✅ Pipeline reliability & monitoring

✅ Scalable analytics systems

You’ll work closely with:

• Data Analysts
• Data Scientists
• Engineering Teams
• BI & Leadership Teams

to ensure the organization always has accurate, clean, and trustworthy data.

If you:

• enjoy building robust data systems,
• love optimizing pipelines and queries,
• and care deeply about data quality and scalability,

this role is a strong fit.

🔥 What You’ll Own

ETL / ELT Pipeline Development

• Build and maintain scalable ETL/ELT pipelines using:

  • Python
  • SQL
  • Scala

• Ingest data from:

  • APIs
  • SaaS platforms
  • relational databases
  • cloud applications
  • streaming systems

• Develop reliable workflows for:

  • data extraction
  • transformation
  • loading
  • validation

Workflow Orchestration & Automation

• Manage orchestration platforms such as:

  • Apache Airflow
  • Prefect
  • Dagster
  • Luigi

• Monitor:

  • pipeline health
  • failed jobs
  • scheduling reliability

• Build automated workflows with:

  • retries
  • alerting
  • dependency management

Data Warehousing & Modeling

• Design and optimize cloud data warehouses using:

  • Snowflake
  • BigQuery
  • Redshift

• Develop:

  • star schemas
  • snowflake schemas
  • analytics-ready data models

• Improve:

  • query performance
  • clustering
  • partitioning
  • warehouse efficiency

Data Quality & Governance

• Implement:

  • validation checks
  • anomaly detection
  • logging systems
  • lineage tracking

• Use tools such as:

  • dbt
  • Great Expectations

• Ensure:

  • consistent naming conventions
  • clean transformations
  • audit-ready datasets

• Support compliance requirements:

  • GDPR
  • HIPAA
  • industry-specific governance standards

Streaming & Real-Time Data

• Build and maintain streaming pipelines using:

  • Kafka
  • Kinesis
  • Pub/Sub

• Support:

  • real-time ingestion
  • event-driven processing
  • low-latency analytics workflows

Infrastructure & DevOps

• Containerize services using:

  • Docker
  • Kubernetes

• Build CI/CD workflows with:

  • GitHub Actions
  • Jenkins
  • GitLab CI

• Manage cloud infrastructure using:

  • Terraform
  • CloudFormation

• Improve scalability, reliability, and deployment automation

Cross-Functional Collaboration

• Partner with:

  • analysts
  • data scientists
  • BI teams
  • product teams

• Deliver curated datasets for:

  • dashboards
  • analytics
  • machine learning workflows

• Support BI tools such as:

  • Tableau
  • Looker
  • Power BI

• Maintain documentation for:

  • pipelines
  • schemas
  • workflows
  • data definitions

✅ Required Experience & Skills

• 3+ years of Data Engineering or backend engineering experience

• Strong proficiency with:

  • Python
  • SQL

• Experience with:

  • Snowflake
  • BigQuery
  • Redshift

• Familiarity with:

  • Airflow
  • Prefect
  • workflow orchestration tools

• Strong understanding of:

  • ETL pipelines
  • data modeling
  • cloud infrastructure
  • warehouse optimization

⭐ Ideal Experience

• Experience using:

  • dbt
  • Great Expectations
  • data lineage tools

• Streaming experience with:

  • Kafka
  • Kinesis
  • Pub/Sub

• Experience with:

  • AWS Glue
  • GCP Dataflow
  • Azure Data Factory

• Background in:

  • healthcare
  • fintech
  • regulated environments

• Experience optimizing large-scale warehouse costs and performance

🧠 What Makes You a Great Fit

• You care deeply about clean and reliable data
• You enjoy debugging complex pipeline and infrastructure issues
• You think about scalability and long-term maintainability
• You combine engineering rigor with analytical thinking
• You communicate effectively across technical and non-technical teams

📅 What a Typical Day Looks Like

• Review Airflow/Prefect pipeline health and resolve failures
• Build connectors for new APIs or SaaS platforms
• Optimize SQL queries and warehouse performance
• Collaborate with analysts and data scientists on datasets
• Improve validation and monitoring systems
• Document pipelines and warehouse structures
• Reduce warehouse costs and improve pipeline reliability

In short:

You build the data infrastructure that powers analytics, reporting, automation, and business intelligence across the organization.

📊 Key Success Metrics (KPIs)

• Pipeline uptime ≥ 99%
• Data freshness within SLA
• Zero critical data quality issues reaching production
• Query performance & warehouse cost optimization
• Reliable and scalable pipeline infrastructure
• Positive feedback from analysts, BI teams, and leadership

🌟 Why This Role Stands Out

• Work on modern cloud-native data infrastructure

• Build scalable ETL and analytics systems

• Exposure to:

  • streaming pipelines
  • cloud data platforms
  • orchestration frameworks
  • warehouse optimization

• Opportunity to grow into:

  • Senior Data Engineer
  • Analytics Engineering
  • Platform Engineering
  • Data Architecture

• Fully remote flexibility with collaborative engineering teams

🧪 Interview Process

• Initial Phone Screen
• Video Interview with Pavago Recruiter
• Technical Task

(Build a small ETL pipeline or optimize a SQL query)

• Client Interview with Engineering/Data Team
• Offer & Background Verification

👉 Apply Now

If you:

• love building scalable data systems,
• enjoy solving complex pipeline problems,
• and want to work with modern data infrastructure,

This role is a strong fit for you.