Director, Data and AI Engineering

Benevity•Calgary, AB

8d•Hybrid

About The Position

Benevity is 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.

Requirements

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
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

Nice To Haves

Master’s or PhD in Computer Science, Data Science, AI/ML, or a related field is preferred

Responsibilities

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.
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.
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.
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.
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.