Data Engineer - Work From Home

About the Company

The hiring client is seeking a Data Engineer to build and maintain dependable data infrastructure that supports business analytics, reporting, and operational decision-making. The role is presented through Pavago's recruitment process and involves working with the client's engineering and data teams.

About the Role

The Data Engineer will design, develop, and maintain scalable data pipelines that move information from source systems into data warehouses, dashboards, and downstream applications. The role focuses on data integration, warehouse optimization, data quality, and reliable data processing.

The successful candidate will combine strong software engineering fundamentals with practical experience in modern data technologies. Working with engineers, analysts, data scientists, and business stakeholders, the Data Engineer will help ensure data remains accurate, accessible, well-documented, and ready for analysis.

Key Responsibilities

• Build, maintain, and optimize ETL/ELT pipelines using Python, SQL, or Scala.
• Orchestrate data workflows using Airflow, Prefect, Dagster, or similar tools.
• Ingest structured and unstructured data from APIs, SaaS platforms, databases, files, and streaming systems.
• Develop scalable data connectors and automated ingestion workflows.
• Manage and optimize cloud data warehouses such as Snowflake, BigQuery, and Redshift.
• Design scalable data schemas using star and snowflake modeling techniques.
• Apply partitioning, clustering, indexing, and other strategies to improve warehouse performance.
• Build clean, analytics-ready datasets for business intelligence and reporting.
• Implement data validation, anomaly detection, logging, and monitoring to maintain data integrity.
• Maintain naming conventions, data lineage, and documentation standards using tools such as dbt or Great Expectations.
• Support audit-ready data processes and compliance with GDPR, HIPAA, or other applicable industry requirements.
• Monitor pipeline health and resolve failures, inconsistencies, and data quality issues.
• Build and maintain real-time data pipelines using Kafka, Kinesis, Pub/Sub, or similar platforms.
• Support low-latency data ingestion and event-driven architectures while optimizing streaming performance and reliability.
• Collaborate with analysts, data scientists, and business stakeholders to deliver 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 covering pipelines, schemas, and workflows.
• Containerize data services using Docker and manage deployments through Kubernetes where applicable.
• Automate deployments through CI/CD tools such as GitHub Actions, Jenkins, or GitLab CI.
• Manage cloud infrastructure using Terraform, CloudFormation, or similar Infrastructure-as-Code tools.
• Improve system performance, scalability, reliability, and cloud cost efficiency.

Required Qualifications

• 3 or more 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 (GCP), or Microsoft Azure.
• Strong debugging and problem-solving skills, with close attention to detail.
• Ability to build clean, reliable, and scalable data systems.
• A balance of software engineering discipline and analytical thinking.
• Ability to collaborate with technical and non-technical stakeholders.
• Proactive communication and ownership of data quality and pipeline reliability.

Preferred Qualifications

• Experience with dbt for data modeling and transformation management.
• Experience building streaming and event-driven data pipelines using Kafka, Kinesis, Pub/Sub, or similar technologies.
• Familiarity with cloud-native data services such as AWS Glue, GCP Dataflow, or Azure Data Factory.
• Experience with Docker, Kubernetes, Terraform, or CI/CD workflows.
• Background in regulated industries, such as healthcare, fintech, or enterprise SaaS.
• Experience optimizing cloud data warehouse costs and query performance at scale.

Work Location and Schedule

This is a full-time, remote position. Working hours align with U.S. client business hours, with flexibility needed for pipeline monitoring, deployments, and data refresh cycles.

Typical Day and Performance Expectations

Daily work includes monitoring pipeline health, troubleshooting failed orchestration jobs, developing ingestion workflows, optimizing SQL queries, and improving warehouse performance. The Data Engineer will also collaborate with analysts and data scientists, implement data validation checks, and document data models and workflows.

Key performance indicators include:

• Pipeline uptime of at least 99%.
• Data freshness maintained within agreed service-level agreements (SLAs).
• Zero critical data quality issues reaching downstream reporting systems.
• Improved warehouse query performance and cloud cost efficiency.
• Timely delivery of scalable, reliable datasets.
• Positive feedback from analysts, data scientists, and business stakeholders.

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.