IT Data Quality Engineering Manager - Fully Remote!

As IT Data QE Engineering Manager at KinderCare Learning Companies, you will drive delivery excellence, embed quality engineering practices across the software development lifecycle (SDLC), and improve measurable data reliability, accuracy, and observability across enterprise data platforms. This role focuses on validation, reliability, and observability of machine‑learning systems rather than model development. KinderCare is looking for a strong leader in modern data platforms and machine‑learning quality validation to ensure the reliability of both data pipelines and ML‑driven analytics products. Responsibilities Databricks & Modern Data Platforms Define and execute data quality strategy supporting Databricks‑based Lakehouse platforms (Delta Lake, Spark, SQL) Validate complex ETL/ELT pipelines across batch and near‑real‑time ingestion workflows Implement automated data validation frameworks integrated into CI/CD pipelines Implement data observability practices including freshness, volume, and schema monitoring Reduce production data defects through early quality gates and proactive monitoring Partner with Data Engineering to improve pipeline performance, scalability, and reliability Machine Learning & Advanced Analytics Lead quality validation strategy for ML pipelines, including training data validation, feature integrity checks, and model output verification Validate ML workflows across experimentation, training, deployment, and monitoring stages within MLOps pipelines Establish processes for model output verification, performance benchmarking, and reproducibility Partner with Data Science and MLOps teams to validate monitoring controls for data drift, bias detection, and model performance degradation Validate ML workflows using tools such as MLflow, Feature Stores, or equivalent ML lifecycle platforms Validate ML workloads executed within Databricks environments including feature pipelines and model inference datasets Collaborate with Data Science teams to enhance explainability and operational reliability of models Data Governance & Enterprise Data Quality Embed governance controls into QE lifecycle (lineage validation, metadata completeness, access control testing) Establish data quality KPIs aligned with enterprise standards Lead root cause analysis for systemic data integrity issues impacting reporting and analytics Leadership & Delivery Excellence Lead cross‑functional quality initiatives spanning Data Engineering, Data Science, and Platform teams Build and mentor high‑performing Data QE teams Promote culture of extreme ownership and accountability Drive cross‑functional alignment between Engineering, Data Science, Product, and Governance Influence roadmap decisions through quality and risk insights Strategic Partnership & Influence Serve as a trusted advisor to engineering and business leadership on delivery strategy, capacity planning, and prioritization Influence roadmap decisions by providing data‑driven insights on sequencing, trade‑offs, and risk exposure Partner with Product and Engineering leaders to align execution plans with long‑term strategic objectives Drive cross‑functional alignment in complex, ambiguous environments by providing insights into capacity, sequencing, and trade‑offs Ensure engineering engagement models evolve to support business growth and innovation Model Reliability & Observability Establish monitoring validation for model performance degradation and drift Define quality gates for model promotion and deployment readiness Ensure reproducibility through dataset and feature version validation Qualifications Bachelor’s degree in computer science, Information Systems, Business, or related discipline (or equivalent experience) 7+ years of experience in Data Engineering, Data QE, or Data Quality roles, 3+ years leading data or quality engineering teams supporting analytics or ML platforms Hands‑on experience with Databricks, Spark, SQL Experience validating ML pipelines including training data quality, feature validation, and model output testing Working knowledge of model evaluation metrics (precision/recall, ROC‑AUC, drift metrics, or equivalent) Validated lineage and traceability across data pipelines and ML feature/model artifacts Experience operating within MLOps or AI‑enabled analytics environments Experience implementing automation within cloud‑based environments Strong experience working with external vendors, system integrators, or offshore delivery teams Strong understanding of software development lifecycle (Agile/Scrum preferred) Proven ability to influence and navigate complex stakeholder environments Strong analytical and problem‑solving abilities Preferred Experience working with cross‑functional enterprise teams Background in technical program management or delivery leadership Familiarity with tools such as Jira, Confluence, or similar tracking systems Benefits Whole‑family support with discounted child care benefits Medical, dental, and vision benefits for your family (and pets) Employee assistance programs for mental health and personal growth Health and wellness programs, paid time off, and discounts for work necessities such as cell phones … and much more Equal Opportunity Employment KinderCare Learning Companies is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, age, sex, religion, disability, sexual orientation, marital status, military or veteran status, gender identity or expression, or any other basis protected by local, state, or federal law. #J-18808-Ljbffr KinderCare Learning Companies