Head of Data Strategy / Chief Data Officer Director / VP

Key Responsibilities

• Data Strategy & Transformation: Define and execute the roadmap to modernize our data architecture. Lead the transition from suboptimal legacy systems to a best-in-class cloud environment, ensuring our infrastructure supports hyper-scale analytics and real-time ML inference.
• Infrastructure Engineering (Hands-on): Architect and implement a robust data lakehouse solution using the AWS ecosystem (S3, Glue, Redshift/Athena), and potentially onboard Databricks. Ensure the pipes are clean, reliable, near real-time, and scalable.
• MLOps & AIOps Leadership: Build the rails for our models to run on. Design the CI/CD pipelines for Machine Learning (using tools like SageMaker), ensuring that our underwriting and operational models can be deployed, monitored, and retrained with minimal friction.
• Data Governance & Quality: Establish the "rules of the road." Implement rigorous data governance standards, ensuring data lineage, security, and quality control are baked into the architecture, not added as an afterthought.
• Operationalizing Intelligence: Champion scientific rigor in data-driven decision-making. Work with Operations, Technology, and Product to embed data signals directly into our operational workflows, moving the company from "reporting on the past" to "predicting the future."

Team Leadership

• Recruit and mentor a team of high-performing data engineers and ML engineers. Foster a culture of technical excellence and curiosity.

What We're Looking For

• Experience: 5+ years in Data Engineering or ML Engineering, with at least 4+ years in a leadership role scaling data platforms.
• Technical Mastery (AWS): Deep, hands-on experience with AWS Sagemaker, Glue, Lambda, Step Functions, and Redshift.
• Strong Programming Skills: Proficiency in Python, Spark, Distributing Computing, and SQL.
• MLOps Expertise: Proven experience building Machine Learning Operations pipelines. Understands complexities of model drift, feature stores, and inference latency, in addition to Bayesian approaches to uncertainty.
• Governance Mindset: Experience implementing data governance frameworks in a regulated industry (FinTech/Banking) is highly preferred.
• Builder Spirit: Thrives in environments where the path isn't fully paved. Enjoys challenges of transforming suboptimal infrastructure into a competitive advantage.