Data Engineer at Pavago in Pakistan, Pakistan

🚀 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.