Senior Data Engineer
Fully Remote
$80-85 per hour
Long Term Contract
 

Our technology team is responsible for delivering scalable, reliable data solutions that support critical business operations and digital products. We value continuous learning, collaboration, and staying current with emerging technologies as our data and technology needs continue to evolve.

As a Senior Data Engineer, you will help ensure that the data powering products, reporting, and operational workflows is accurate, complete, timely, and trustworthy. You will design and implement data quality checks, monitoring, and remediation processes across data pipelines and platforms.

This role combines hands-on data engineering, technical investigation, automation, data analysis, and cross-functional collaboration.

Data Quality Engineering & Monitoring

• Design and implement automated data quality checks for completeness, accuracy, consistency, freshness, and schema integrity.
• Build monitoring, alerting, and observability solutions to identify anomalies, pipeline failures, data drift, and unexpected changes.
• Develop and maintain reconciliation processes across source systems, transformed datasets, reports, and operational outputs.
• Partner with engineers, analysts, and stakeholders to define quality rules, acceptance criteria, and data validation requirements.
• Create reusable frameworks, scripts, and tools for data profiling, testing, and validation.

Investigation, Analysis & Remediation

• Investigate data issues by tracing data across systems, transformations, and business workflows to identify root causes.
• Use SQL, Python, and cloud data tools to analyze large datasets, isolate anomalies, and validate business logic.
• Support incident response and resolution for data-related production issues.
• Partner with cross-functional teams to remediate defects and reduce recurring data issues.
• Communicate findings, recommendations, and data quality trends to technical and non-technical stakeholders.

Governance, Documentation & Team Success

• Document data definitions, validation logic, lineage, quality rules, and remediation procedures.
• Contribute to best practices for testing, version control, deployment, and maintenance of data quality solutions.
• Participate in Agile ceremonies, code reviews, and team planning.
• Contribute to standards for data governance, ownership, and operational excellence.
• Partner with stakeholders to improve trust in shared data assets and incorporate data quality into the development lifecycle.

• Bachelor's degree in Engineering, Computer Science, or a related field, or equivalent experience.
• 10+ years of professional experience in data engineering, data quality, data testing, analytics engineering, software engineering, or a related field.
• Expert-level SQL skills, including complex queries for data analysis, validation, reconciliation, and troubleshooting.
• Strong Python or TypeScript skills for automation, testing, and data analysis.
• Experience designing or maintaining data quality checks, monitoring, alerting, or observability processes.
• Experience with cloud data platforms and technologies such as AWS, Amazon Redshift, Amazon Athena, Snowflake, or Databricks.
• Experience working with large datasets and big-data processing technologies such as Apache Spark.
• Strong understanding of data structures, data modeling, transformations, lineage, and common sources of data defects.
• Experience investigating data issues across multiple systems and translating business requirements into structured analysis and solutions.
• Experience with at least one AI-assisted development or analytics tool, such as Amazon Q, Google Cloud AI, Google Cloud Smart Analytics, Tableau AI, GitHub Copilot, or Claude.
• Strong communication, documentation, collaboration, and problem-solving skills.

• Experience with AWS S3, AWS Glue, AWS Athena, AWS Lambda, and AWS SNS/SQS.
• Experience with Tableau Desktop/Creator or Amazon QuickSight for data analysis and reporting.
• Experience developing or deploying AI-assisted solutions or AI agents to automate data pipelines, coding, testing, or analytical workflows.
• Experience with data governance, data lineage, and data observability.
• Strong Excel skills.
• Experience supporting production data environments and high-priority operational issues.
• Strong leadership, interpersonal, and consultative skills.
• Highly self-motivated with strong attention to detail and a continuous-learning mindset.