Disclaimer: The base salary range represents the low and high end of Altus Group’s “Pay Grade Range” for this position in the primary work location. Actual hiring salaries will vary depending on factors including but not limited to work experience, and geographic market data for the role. The Pay Grade Range listed above does not reflect Altus Group’s total compensation for employees. Other rewards may include an annual bonus, flexible work arrangements, and region-specific benefits.

Unlock your Altus Experience!

If you’re looking to advance your career in data analytics, expertise, and technology for the rapidly growing global CRE market, there’s no better place than Altus Group. At Altus, our work is purposeful. Every day, our employees drive impact, innovate, and shape the global commercial real estate (CRE) and PropTech industry.

Our people-centric culture empowers you to deliver in a high trust, high performance culture, surrounded by an inclusive team that’s collaborating to modernize our industry. We invest in our people with training and growth opportunities designed to propel you further in your career while providing a flexible and progressive workplace that reflects our values and teams.

Job Summary:

As the Lead Analytics Engineer, you will lead the function that bridges data engineering, advanced analytics, and machine learning within our Data and Analytics platform. In this role, you’ll focus on building ML-ready data infrastructure, developing feature stores, and leading teams that transform raw data into actionable insights powering both traditional analytics and AI/ML initiatives. You’ll define the strategic direction for analytics infrastructure, statistical modeling frameworks, and ML operationalization, collaborating closely with product, architecture, data science, software engineering, and business teams across Altus.

Key Responsibilities:

Build, mentor, and scale the analytics engineering team across data modeling, feature engineering, and ML infrastructure
Define and execute the strategic roadmap for analytics engineering capabilities supporting both traditional BI and AI/ML initiatives
Establish analytics engineering standards, governance, and best practices across the organization
Drive cross-functional collaboration with product, data science, software engineering, and business stakeholders to translate requirements into scalable data products and analytics solutions
Design and oversee implementation of scalable feature stores and ML-ready data architectures
Lead development of core analytical datasets, feature pipelines, and training data infrastructure
Ensure data accuracy, consistency, and performance across all analytical data products
Guide implementation of data transformation pipelines using modern analytics engineering tools (dbt, Dataform, etc.)
Drive creation of reusable feature libraries, model registries, and self-service analytics capabilities
Oversee development of automated testing, data lineage documentation, and business glossaries
Establish monitoring, alerting, and SLA frameworks for all analytics engineering deliverables
Implement data quality frameworks and automated validation processes

Key Qaulifications:

Deep expertise in analytics engineering, statistical modeling, and building ML infrastructure at scale
Proven track record leading teams that support both traditional analytics and advanced ML use cases
Expert-level SQL and Python skills with experience in statistical analysis and ML feature engineering
Hands-on experience with analytics and ML tools: dbt, Feast, Tecton, MLflow, Kubeflow, Airflow
Experience building and deploying ML models in production: classification, regression, clustering, deep learning
Expertise in feature engineering, feature stores, and building real-time ML data pipelines
Experience with distributed computing frameworks (Spark, Dask, Ray) for large-scale analytics and ML
Knowledge of advanced analytics techniques: survival analysis, Bayesian methods, ensemble modeling
Experience with streaming analytics and real-time feature computation (Kafka, Flink, Spark Streaming)
Proven ability to design cost-effective ML infrastructure and optimize compute resources for training and inference
Understanding of model monitoring, drift detection, and automated retraining strategies
Experience with cloud ML platforms (SageMaker, Azure ML) and their integration patterns
Experience translating complex business requirements into scalable technical solutions

What Altus Group offers:

Rewarding performance: We are pleased to be able to provide employees competitive compensation, incentive and bonus plans, and a total rewards package that prioritizes their mental, physical and overall financial health.
Growth and development: As a destination for top industry talent, we’re investing in you to meet the evolving needs of our clients and deliver on your professional goals. Our Altus Intelligence Academy offers over 150,000 hours of learning materials catering to diverse stages of an employee’s career journey.
Flexible work model: We’re modernizing our employee programs to reflect the new world of work. Our Activity-Based Work model provides you with flexibility to align your work location to the work being performed - office for connecting and collaborating, and remote for focused work.

Altus Group is committed to fostering an inclusive work environment where all clients and employees feel welcomed, accepted and valued. We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.

Applicants with disabilities may contact Altus Group to request and arrange for accommodations. If you need accommodation, please contact us at [email protected] or +1 888 692 7487.

We appreciate all applicants who take the time to apply to Altus Group. Please note that only those who are selected to move forward in the process will be contacted. Thank you.

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