We are seeking a Director of Data and ML/AI Engineering to lead our data and AI technology strategy, architecture, and execution within our B2B SaaS Benevity Impact Platform. This role is pivotal in enabling data-driven decision-making, product intelligence, and AI-powered services and automation across our platform.
The ideal candidate will have deep expertise in scalable data engineering, AI/ML systems and services, and cloud-based data architectures, with a track record of building highly available, real-time analytics, and ML/AI-driven solutions for SaaS platforms. This role will oversee data science, data modeling, business intelligence, governance, and ML/AI systems engineering, ensuring alignment with our client and business needs.
What you'll do:Leadership & Data Strategy- Define and execute the data and AI strategy for our SaaS platform, ensuring alignment with business objectives and customer needs.
- Lead cross-functional teams across data engineering, AI/ML, BI, and governance to deliver scalable, high-impact solutions.
- Establish data-as-a-product principles, ensuring data assets are secure, reusable, and monetizable within our platform.
- Drive innovation in real-time analytics, AI-powered automation and intelligence for clients.
- Partner with Product, Engineering, and Customer Success teams to enhance data-driven insights, reporting, and self-service analytics.
Data Engineering & Platform Architecture- Design and oversee high-scale, cloud-native data architectures that support real-time streaming, data lakes / lakehouses, and enterprise warehousing.
- Implement highly available, fault-tolerant data pipelines to process, store, and serve insights across the platform.
- Lead data modeling initiatives to optimize data structures for analytics, ML training, and business reporting.
- Work with engineering teams to embed AI-powered features into the SaaS platform, enabling smart automation, anomaly detection, and personalization.
- Drive observability, monitoring, and cost optimization across our cloud-based data infrastructure.
AI/ML Engineering & Data Science- Oversee the development of ML/AI models, ensuring they are scalable, explainable, and deployed effectively in production.
- Implement MLOps best practices, including CI/CD for models, versioning, and real-time inference.
- Lead initiatives in AI-powered recommendations, predictive analytics, NLP, and generative AI to enhance our product offerings.
- Ensure bias-free, ethical AI practices and compliance with industry AI standards and regulations.
Business Intelligence & Product Analytics- Build and manage BI tools, dashboards, and self-service analytics to provide real-time insights to clients and internal stakeholders.
- Develop product analytics frameworks, tracking user behavior, adoption, and feature engagement.
- Implement embedded analytics solutions that allow clients to gain actionable insights from their data.
- Optimize reporting for SaaS metrics (MRR, ARR, churn, retention, LTV, NPS) and drive data-informed decision-making.
Data Governance & Compliance- Own data governance frameworks, ensuring data privacy, security, and compliance (GDPR, SOC 2, PIPEDA, CCPA, etc.).
- Define and implement data lineage, metadata management, and master data management (MDM) best practices.
- Drive role-based access controls (RBAC, ABAC or ReBac) and ensure secure, auditable data sharing within our SaaS ecosystem.
- Champion data democratization, enabling teams to access trusted, well-documented data.
What you'll bring:- 10+ years of experience in data engineering, AI/ML, or analytics leadership roles, ideally in B2B SaaS or cloud-based platforms
- Strong expertise in scalable data architectures, ML/AI systems, and embedded analytics
- Proven track record in leading high-performance teams, managing budgets, and delivering data-driven product innovations
- Experience with multi-tenant SaaS data governance, compliance (SOC 2, GDPR, etc.), and customer-facing analytics
- Familiarity with emerging AI trends, including LLMs, Generative AI, and responsible AI frameworks
- Familiarity with SaaS product analytics and AI-powered automation use cases
- Master's or PhD in Computer Science, Data Science, AI/ML, or a related field is preferred
Technical Skills & Expertise- Data & AI Stacks (Cloud-Native, SaaS-Optimized)
- Data Engineering & Storage: BigQuery, Redshift, Delta Lake, Snowflake, Databricks
- AI/ML Frameworks: Azure AI, TensorFlow, PyTorch, Hugging Face, MLflow, Vertex AI
- MLOps & Pipelines: Kubeflow, SageMaker, Airflow, Feature Stores, DataRobot
- BI & Analytics Tools: Looker, Power BI, Mode Analytics, Amplitude, Mixpanel
- Data Governance & Security: Collibra, Informatica, Alation, Immuta
- Cloud & DevOps: AWS (S3, Lambda, Kinesis), GCP (BigQuery, Pub/Sub), Kubernetes, Terraform
- Programming: Python, SQL, Scala, Java
- Streaming & Real-Time Data: Kafka, Flink, Apache Beam
