Data Engineer

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

- Application Review

- Spark Hire Intro Video (3–5 minutes)

- Technical Assessment (ETL Pipeline or SQL Exercise)

- Client Interview

- Offer & Onboarding

What Happens After You Apply

After submitting your application, you’ll receive an email invitation from Spark Hire to record