IT Data Quality Engineering Manager - Fully Remote!

Futures start here. Where first steps, new friendships, and confident learners are born. At KinderCare Learning Companies, the first and only early childhood education provider recognized with the Gallup Exceptional Workplace Award , we offer a variety of early education and child care options for families. Whether it's KinderCare Learning Centers, Champions, or Crème de la Crème, we build confidence for kids, families, and the future we share. And we want you to join us in shaping it—in neighborhoods, at work, and in schools nationwide. At KinderCare Learning Companies, you'll use your skills and expertise to support the work (and fun) that happens in our sites and centers every day. From marketers and strategists to financial analysts and data engineers, and so much more, we're all passionate about crafting a world where children, families, and organizations can thrive. KinderCare is looking for strong leader in modern data platforms and machine learning quality validation to ensure reliability of both data pipelines and ML-driven analytics products. As IT Data QE Engineering Manager, you'll drive delivery excellence, embed quality engineering practices across the SDLC, and improve measurable data reliability, accuracy, and observability across enterprise data platforms. This role focuses on validation, reliability, and observability of ML systems rather than model development.

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 explain ability and operational reliability of models Data Governance & Enterprise Data Quality Embedding 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, sequence and tradeoffs 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 both 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 #LI-Remote Our benefits meet you where you are. We're here to help our employees navigate the integration of work and life:

• Know your whole family is supported with discounted child care benefits.
• Breathe easy with medical, dental, and vision benefits for your family (and pets, too!).
• Feel supported in your mental health and personal growth with employee assistance programs.
• Feel great and thrive with access to health and wellness programs, paid time off and discounts for work necessities, such as cell phones.
• … and much more.

We operate research-backed, accredited, and customizable programs in more than 2,000 sites and centers across 40 states and the District of Columbia. As we expand, we're matching the needs of more and more families, dynamic work environments, and diverse communities from coast to coast. Because we believe every family deserves access to high-quality child care, no matter who they are or where they live. Every day, you'll help bring this mission to life by building community and delivering exceptional experiences. And if you're anything like us, you'll come for the work, and stay for the people. 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.