About the Role
Pavago’s client is seeking a Data Engineer to design, build, and maintain scalable data infrastructure and reliable pipelines that support analytics, reporting, and operational decision-making.
This full-time remote role requires strong software engineering fundamentals and hands-on experience with modern data technologies. You will work across engineering, analytics, and business teams to ensure data moves reliably from source systems into warehouses, dashboards, and downstream applications while maintaining high standards for data quality, governance, performance, and scalability.
Key Responsibilities
Pipeline Development and Data Integration
Build, maintain, and optimize ETL/ELT pipelines using Python, SQL, or Scala.
Orchestrate workflows using Airflow, Prefect, Dagster, or similar tools.
Ingest structured and unstructured data from APIs, SaaS platforms, databases, files, and streaming systems.
Develop scalable connectors and automated data ingestion workflows.
Data Warehousing and Modeling
Manage and optimize cloud data warehouses, including Snowflake, BigQuery, and Redshift.
Design scalable data schemas using star and snowflake modeling techniques.
Implement partitioning, clustering, indexing, and other performance optimization strategies.
Build clean, analytics-ready datasets for business intelligence and reporting.
Data Quality, Governance, and Reliability
Implement validation checks, anomaly detection, logging, and monitoring to maintain data integrity.
Establish naming conventions, lineage tracking, and documentation standards using tools such as dbt or Great Expectations.
Maintain audit-ready data processes and support compliance with GDPR, HIPAA, or applicable industry requirements.
Monitor pipeline health and proactively resolve failures and data inconsistencies.
Streaming and Real-Time Data Processing
Build and manage real-time data pipelines using Kafka, Kinesis, Pub/Sub, or similar platforms.
Support low-latency data ingestion and event-driven architectures.
Monitor streaming infrastructure and optimize throughput and reliability.
Collaboration and Analytics Enablement
Work with data analysts, data scientists, and business stakeholders to provide reliable datasets.
Support dashboard and reporting initiatives using Tableau, Looker, or Power BI.
Translate business requirements into scalable data solutions and models.
Maintain clear technical documentation for pipelines, schemas, and workflows.
Infrastructure, DevOps, and Automation
Containerize data services using Docker and manage deployments through Kubernetes when applicable.
Automate deployments using CI/CD tools such as GitHub Actions, Jenkins, or GitLab CI.
Manage cloud infrastructure using Terraform, CloudFormation, or similar Infrastructure-as-Code tools.
Improve performance, scalability, reliability, and cloud cost efficiency.
Required Qualifications
At least 3 years of experience in data engineering, back-end engineering, or data infrastructure roles.
Strong proficiency in Python and SQL.
Experience with at least one modern data warehouse, such as Snowflake, Redshift, or BigQuery.
Hands-on experience with workflow orchestration tools such as Airflow or Prefect.
Strong understanding of ETL/ELT pipelines, data modeling, and data transformation workflows.
Familiarity with cloud platforms such as AWS, Google Cloud Platform, or Azure.
Preferred Qualifications
Experience with dbt for data modeling and transformation management.
Experience building streaming and event-driven data pipelines using Kafka, Kinesis, or Pub/Sub.
Familiarity with cloud-native data services such as AWS Glue, Google Cloud Dataflow, or Azure Data Factory.
Experience with Docker, Kubernetes, Terraform, or CI/CD workflows.
Background in regulated industries such as healthcare, financial technology, or enterprise SaaS.
Experience optimizing data warehouse costs and query performance at scale.
What Makes You a Good Fit A strong interest in building clean, reliable, and scalable data systems.
A practical debugging and problem-solving approach with close attention to detail.
The ability to combine software engineering discipline with analytical thinking.
Comfort working with both technical and non-technical stakeholders.
Proactive communication and a willingness to take ownership of data quality and reliability.
A Typical Day in This Role
Monitor pipeline health and troubleshoot failed jobs in Airflow or similar orchestration systems.
Build and maintain ingestion pipelines for APIs, SaaS platforms, and operational databases.
Optimize SQL queries and warehouse performance to improve efficiency and reduce cloud costs.
Collaborate with analysts and data scientists to provide curated datasets for reporting and modeling.
Implement validation checks and monitoring to prevent downstream data quality issues.
Document data models, transformations, and workflows to support long-term maintainability.
Key Performance Indicators
Pipeline uptime of at least 99%.
Data freshness maintained within agreed service-level agreements.
No critical data quality issues reaching downstream reporting systems.
Improved warehouse query performance and cost efficiency.
Timely delivery of scalable, reliable datasets.
Positive feedback from analysts, data scientists, and business stakeholders.
Work Arrangement and Employment Details
Employment type: Full-time.
Work arrangement: Remote.
Working hours: U.S. client business hours, with flexibility for pipeline monitoring, deployments, and data refresh cycles.
Interview Process
Initial phone screening.
Video interview with a Pavago recruiter.
Technical assessment, such as building a small ETL pipeline or optimizing a SQL query.
Client interview with the engineering or data team.
Offer and background verification.