IT Data Quality Engineering Manager - Fully Remote

Futures start here. Where first steps, new friendships, and confident learners are born. At Kinder Care 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 Kinder Care 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 Kinder Care 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.

Kinder Care is looking for a 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.

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…