We are looking for a seasoned Data Architect (Senior Engineer) to lead the design and implementation of scalable, secure, and privacy-compliant data architectures that support feedback-driven AI systems. This role will focus on developing robust data ingestion pipelines, managing complex data flows, and integrating frameworks for model evaluationincluding fairness, bias, drift analysis, and KPI visualization. The ideal candidate brings both deep technical expertise in modern data architectures and a strong grasp of responsible AI practices. Key Responsibilities: Data Architecture & Ingestion: Design, build, and optimize data ingestion pipelines for high-volume, real-time, and batch dataparticularly focused on user feedback and model output data. Architect and implement data pipelines that comply with data privacy, security, and governance policies , ensuring encryption, anonymization, and access control. Define data modeling standards for structured, semi-structured, and unstructured data to support training, inference, and monitoring use cases. Collaborate with ML engineers and data scientists to ensure seamless flow of feedback data into model evaluation and retraining loops. Data Handling & Compliance: Implement and enforce best practices in data lifecycle management , including classification, retention, and secure disposal. Ensure compliance with data regulations such as GDPR, HIPAA, and other regional privacy standards . Work closely with security and legal teams to conduct data privacy impact assessments (DPIAs) and ensure audit readiness. Model Evaluation & Monitoring: Support data scientists by enabling access to relevant features and data slices for model performance evaluation, bias detection, and drift monitoring. Build or integrate tools for model benchmarking and fairness audits , enabling transparency and accountability in deployed AI systems. Develop dashboards or visualization layers that track model KPIs , performance metrics, and data health indicators over time. Required Qualifications: Bachelor's or master's degree in computer science, Data Engineering, or a related technical field. 6+ years of experience in data engineering, architecture, or analytics, including at least 2 years in a senior or lead role. Proven experience designing and building data ingestion pipelines using technologies like Apache Kafka, Apache Airflow, Spark, or cloud-native tools (e.g., AWS Glue, GCP Dataflow). Strong understanding of data privacy laws and security best practices , including encryption, tokenization, RBAC, and secure data zones. Familiarity with tools and frameworks for model evaluation and monitoring (e.g., Evidently AI, Fiddler, WhyLabs, MLflow, or custom dashboards). Proficiency in SQL and Python; experience with data visualization libraries (e.g., Plotly, Dash, D3.js) is a plus. Preferred Qualifications: Experience working with feedback loops in production AI/ML systems (e.g., user ratings, action logs, predictions vs outcomes). Familiarity with MLOps practices and how data pipelines interface with CI/CD and model retraining workflows. Knowledge of responsible AI principles and bias/fairness mitigation strategies . Strong communication and stakeholder management skills, with experience presenting technical concepts to non-technical audiences.