About the Company
Our client is seeking a Data Engineer to strengthen its data infrastructure and support analytics, reporting, and operational decision-making across the business. The role focuses on building reliable data pipelines and scalable systems that make accurate, timely data accessible to teams across the organization.
About the Role The Data Engineer will design, develop, and maintain data pipelines that move information from source systems into cloud data warehouses, dashboards, and downstream applications. The position requires strong software engineering fundamentals, hands-on experience with modern data technologies, and a focus on data quality, governance, performance, and reliability.
You will work closely with data analysts, data scientists, engineers, and business stakeholders to turn requirements into dependable data solutions. The role also involves monitoring pipeline performance, resolving data issues, improving cloud efficiency, and maintaining clear technical documentation.
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 connectors and automated data ingestion workflows.
Manage and optimize cloud data warehouses, including Snowflake, BigQuery, and Redshift.
Design scalable data schemas using star and snowflake modeling techniques.
Apply partitioning, clustering, indexing, and other strategies to improve query performance.
Prepare clean, analytics-ready datasets for business intelligence and reporting.
Implement data validation checks, anomaly detection, logging, and monitoring to maintain data integrity.
Maintain naming conventions, data lineage, and documentation 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 proactively troubleshoot failures, inconsistencies, and data quality issues.
Build and manage real-time data pipelines using Kafka, Kinesis, Pub/Sub, or similar platforms.
Support low-latency ingestion and event-driven architectures, optimizing streaming throughput and reliability.
Collaborate with analysts, data scientists, and business stakeholders to deliver reliable datasets and support reporting initiatives across Tableau, Looker, or Power BI.
Translate business requirements into scalable data solutions and maintain documentation for pipelines, schemas, and workflows.
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 system 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 cloud 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, attention to detail, and a commitment to data quality and reliability.
Ability to work across technical and non-technical teams, communicate proactively, and take ownership of data systems.
Preferred Qualifications
Experience with dbt for data modeling and transformation management.
Experience building streaming and event-driven pipelines using Kafka, Kinesis, or Pub/Sub.
Experience with cloud-native data services such as AWS Glue, GCP Dataflow, or Azure Data Factory.
Familiarity with Docker, Kubernetes, Terraform, or CI/CD workflows.
Experience in regulated industries such as healthcare, fintech, or enterprise SaaS.
Experience optimizing data warehouse costs and query performance at scale.
Location and Work Arrangement
Full-time, remote position.
Working hours align with U.S. client business hours, with flexibility for pipeline monitoring, deployments, and data refresh cycles.
Key Performance Indicators
Pipeline uptime of at least 99%.
Data freshness maintained within agreed service-level agreements (SLAs).
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.
Typical Day-to-Day Activities
Monitor pipeline health and troubleshoot failed jobs in Airflow or related orchestration tools.
Build and maintain ingestion pipelines for APIs, SaaS platforms, and operational databases.
Optimize SQL queries and warehouse performance to improve efficiency and manage 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 maintainability and scalability.
Interview Process
Initial phone screen.
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.