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.