Senior Data Engineer

Position: Senior Data Engineer

Mode: Contract - long term

Location: remote - available for EST business hours

visa candidates acceptable? Yes

Team: Interflow /DAP Reporting: DAP Reporting is responsible for reports, analytics data on the digital test administration. Interflow works on generating reports for all the digital test administration. These reports are used to understand the health of the administration and report stats for leadership. This team is responsible for providing live reports to check on the status of the exams and provide canned post admin reports to analyze the admins in detail.

Profile: Data Analyst will work closely with Architects and leads in the Interflow team, key business stakeholders including program staff to support the organizations effort in the areas of data Analysis and information management. Role should have strong experience in writing super complex SQL queries to extract data for reports. Candidate should have experience working with large set of data. These reports are very critical for the company, and we are looking for an expert who is capable of handling, consuming and extracting large sets of data for analysis and reports.

Process: Video (.5 w/Manager - Team 1.30) Remote Interview Protocol - verification including internet policy - start

SQL, Level 5

Python, Level - 5

AI Tools, Level 3

Redshift, Level 5

msExcel, Level - 4

AWS - S3, Level - 3

Glue, Level - 3

AWS - Athena, Level - 2

AWS - Lambda, Level - 1

AWS - SNS/SQS, Level - 1

Tableau Desktop /Creator, Level - 2

QuickSight, Level - 2

We use Tableau and Quicksight for Data Analysis; Apache Spark for Big Data Processing; and they should have used at least one AI tool: Amazon Q; Google Cloud AI; Google Cloud Smart Analytics; Tableau AI

About The Opportunity

As a Senior Data Engineer, you ll help ensure that the data powering our products, reporting, and operational workflows is accurate, complete, timely, and trustworthy. You ll design and implement data quality checks, monitoring, and remediation processes across pipelines and platforms, working closely with engineers, analysts, product teams, and business stakeholders. Your work will help identify issues early, reduce operational risk, and improve confidence in the data used to support critical decisions and digital assessment experiences. This role blends hands-on technical investigation, automation, and cross-functional collaboration.

In This Role You Will

Data Quality Engineering & Monitoring (45%)

• Design and implement automated data quality checks for completeness, accuracy, consistency, freshness, and schema integrity across critical datasets and pipelines.
• Build monitoring, alerting, and observability solutions to detect anomalies, pipeline failures, data drift, and unexpected changes before they impact downstream consumers.
• Develop and maintain reconciliation processes across source systems, transformed datasets, reports, and operational outputs.
• Partner with engineers and analysts to define quality rules, acceptance criteria, and data validation requirements for new and existing systems.
• Create reusable frameworks, scripts, and tooling for profiling, testing, and validating data in production and non-production environments.
• Investigation, Analysis, & Remediation (35%)
• Investigate data issues by tracing data across systems, transformations, and business workflows to identify root causes and recommend fixes.
• Use SQL, Python, and cloud data tools to analyze large datasets, isolate anomalies, and validate business logic.
• Support incident response and issue resolution for data-related production problems, especially during high-priority operational periods.
• Work with cross-functional teams to remediate defects, improve upstream processes, and reduce recurrence of common data issues.
• Communicate findings clearly to both technical and non-technical stakeholders, including issue summaries, remediation recommendations, and quality trends.
• Governance, Documentation, & Team Success (20%)
• Document data definitions, validation logic, lineage, quality rules, and remediation procedures to improve transparency and operational readiness.
• Contribute to best practices for testing, version control, deployment, and ongoing maintenance of data quality solutions.
• Participate in Agile ceremonies, code reviews, and team planning, helping break work into manageable tasks and improve team productivity.
• Support the development of standards for data governance, ownership, and operational excellence across the team.
• Partner with stakeholders to improve trust in shared data assets and ensure quality considerations are built into delivery from the start.

About You

• BS degree in Engineering, Computer Science, or related field / equivalent experience
• 10+ years of general experience in quality testing
• Strong SQL skills and experience writing complex queries to analyze, validate, and troubleshoot data across multiple systems.
• Professional experience in data engineering, analytics engineering, data quality, software Engineering, or a related field with a strong focus on data investigation and validation.
• Exposure to AI-assisted development tools (e.g., GitHub Copilot, Claude) and hands-on experience applying to build and deploy AI agents that automate data pipelines, write code and testing workflows.
• Experience working with cloud data platforms and tools such as AWS, Redshift, Athena, Snowflake, Databricks, or similar technologies.
• Proficiency in Python or type script language used for automation, testing, and data analysis.
• Experience designing or maintaining data quality checks, monitoring, alerting, or observability processes for production datasets or pipelines.
• Strong understanding of data structures, data modeling, transformations, lineage, and common sources of data defects.
• Ability to investigate issues across systems, apply business logic, and translate ambiguous problems into structured analysis and action.
• Experience working with BI/reporting tools such as Tableau, QuickSight, or similar platforms is helpful.
• Strong communication, documentation, and collaboration skills, with the ability to work effectively across technical and non-technical teams.
• A learner s mindset, curiosity about emerging technologies and AI-enabled tools, and a drive to improve systems and processes continuously.
• Ability to support high-priority operational periods and respond effectively to production data issues when needed.
• Strong interpersonal and consultative skills.
• Highly self-motivated and directed, with keen attention to detail.
• Strong leadership skills and customer satisfaction orientation.