Head of Data Strategy / Chief Data Officer Director / VP

ClarityPay•New York City, NY

About The Position

We give businesses and their customers peace of mind by solving complex credit challenges with precision, speed, and intelligence, combining deep expertise with advanced technology, to simplify the experience and deliver better outcomes, every time. We're a fast-growing fintech empowering enterprise merchants with smarter, more adaptive pay-over-time solutions. From point-of-sale financing to “Buy Now, Pay Later” programs and loyalty integrated offers, we’re building configurable credit tools that help businesses serve more of their customers. We value teamwork, clarity of purpose, and rigorous attention to data to drive action. We balance speed and excellence to deliver an exceptional customer experience. Role Overview: ClarityPay is undertaking a transformative approach to our data strategy, tech stack, and data products and platforms. We are looking for a Head of Data Strategy to architect and build our future state. This is not a traditional, hands-off executive role. We possess a data infrastructure that requires significant upliftment to meet the demands of our scale. We need a visionary leader who is equally passionate about writing code as they are about defining strategy. You will lead the migration from a legacy setup to a modern, scalable AWS ecosystem, establishing the data governance, MLOps, and AIOps frameworks that will allow us to become a truly data and intelligence driven operations organization. You will be a catalyst for our healthy and growing business, taking ownership of the entire data lifecycle—from ingestion to insight to automated decision-making.

Requirements

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. You should be comfortable discussing partition strategies in S3 one minute and presenting to the Board the next.
Strong Programming Skills: Proficiency in Python, Spark, Distributing Computing, and SQL is non-negotiable. You must be able to create, maintain, audit code and contribute to the repository when necessary.
MLOps Expertise: Proven experience building Machine Learning Operations pipelines. You understand the complexities of model drift, feature stores, and inference latency, in additional to Bayesian approaches to uncertainty.
Governance Mindset: Experience implementing data governance frameworks in a regulated industry (FinTech/Banking) is highly preferred.
Builder Spirit: You thrive in environments where the path isn't fully paved. You enjoy the challenge of taking a "suboptimal" infrastructure and turning it into a competitive advantage.

Nice To Haves

Governance Mindset: Experience implementing data governance frameworks in a regulated industry (FinTech/Banking) is highly preferred.

Responsibilities

Data Strategy & Transformation: Define and execute the roadmap to modernize our data architecture. You will 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. You will ensure the pipes are clean, reliable, near real-time, and scalable.
MLOps & AIOps Leadership: Build the rails for our models to run on. You will 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." You will 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. We believe in empowering our people to be successful, and you will foster a culture of technical excellence and curiosity.