Senior Data Quality AnalystThe Senior Data Quality Analyst is a hands-on data quality engineer responsible for building automation-first controls, monitoring, and remediation at scale.
The role designs, scripts, and operationalizes data quality validation pipelines, integrates with observability platforms, and leverages APIs to orchestrate end-to-end workflows across cloud and on-prem environments.The ideal candidate is fluent in Python and SQL, experienced with REST/GraphQL APIs, and comfortable integrating DQ checks into ETL/ELT, CI/CD, and event-driven architectures. This is a role with location options in Buffalo, NY or Wilmington, DE.Position ResponsibilitiesEngineer automated data quality pipelines that detect, diagnose, and remediate data defects; design for scalability, idempotency, and observability.Use Python/SQL for data profiling, rule evaluation, schema validation, anomaly detection, and automated exception workflows.Implement API-driven integrations (REST/GraphQL/SDKs) with DQ, catalog, ticketing, notification, and orchestration systems; handle OAuth2, pagination, rate limits, retries, and backoff.Define and maintain DQ rule libraries and validation frameworks as reusable, versioned assets; enforce code quality via unit tests, linting, and static analysis.Embed automated controls into ETL/ELT and data integration jobs; implement pre/post-load validations, row/column-level checks, and SLA/SLO monitoring.Build event-driven alerts and webhook workflows to route exceptions to the right queues (e.g., Jira/ServiceNow), with metadata for reproducibility and audit.Develop KPI dashboards and operations apps using Power BI and Power Apps to visualize coverage, drift, and rule performance, and to streamline triage/remediation.Conduct root-cause analysis using lineage, logs, and metrics; implement automated remediation where feasible (e.g., rollback, quarantine, replay).Contribute to CI/CD workflows (GitLab or equivalent) for DQ assets: automated tests, environment promotion, secrets management, and change controls.Author and maintain runbooks, design docs, and operational playbooks; mentor analysts on scripting, APIs, and engineering best practices.Uphold risk, regulatory, and internal control requirements; ensure auditability and traceability throughout DQ processes.Minimum Qualifications RequiredBachelor’s degree and a minimum of 5 years related experience; or in lieu of a degree, a combined minimum of 9 years higher education and/or work experience, including a minimum of 5 years related experience.Advanced proficiency in Python and SQL for automation, including building reusable libraries, scheduled jobs, and validation pipelines.API engineering experience: designing and consuming REST/GraphQL APIs, handling auth (OAuth2/Bearer), pagination, rate limits, error handling/retries, and webhooks for event-driven processes.Experience building solutions with Power BI and Power Apps for operational reporting and workflow automation.Demonstrated success collaborating across engineering, governance, and business teams in fast-paced environments.Ideal Qualifications PreferredExperience with automated ETL/ELT validation and data integration platforms; familiarity with pre/post-load checks, data contracts, and schema enforcement.Proficiency with DQ/observability platforms (e.g., Informatica Cloud DQ, Monte Carlo, Anomalo, Collibra OwlDQ) and their SDKs/APIs.Hands-on data profiling, rule design, and automated measurement of DQ dimensions (completeness, validity, accuracy, timeliness, uniqueness, consistency).Implemented governance-aligned DQ frameworks (standards, metadata contracts, lineage, policy as code).Experience with workflow/automation tools (Power Apps, Alteryx, or equivalent) and messaging/queueing for event-driven triage.Cloud experience (Azure, Snowflake): scripting with providers/SDKs, secrets management, job orchestration, and cost-aware design.CI/CD in GitLab (or similar): pipeline design, automated testing, code scanning, environment promotion, and approvals.Exposure to AI/ML-based anomaly detection or statistical monitoring; ability to integrate model outputs via APIs.Excellent communication skills—able to explain engineering decisions and automation tradeoffs to non-technical stakeholders.Proven ability to manage multiple concurrent automation initiatives and deliver high-quality solutions on schedule.