IT Data Quality Engineering Manager - Fully Remote

As IT Data QE Engineering Manager at Kinder Care 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.

Kinder Care 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.

• 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

• 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

• 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

• 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

• 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

• 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

• 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…