Lead Analytics Engineer

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Pay Grade Range:

Disclaimer: The base salary range represents the low and high end of Altus Groups 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 Groups total compensation for employees. Other rewards may include an annual bonus flexible work arrangements and region-specific benefits.

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If youre looking to advance your career in data analytics expertise and technology for the rapidly growing global CRE market theres 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 thats 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 this role youll 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. Youll 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 were investing in you to meet the evolving needs of our clients and deliver on your professional goals. Our Altus Intelligence Academy offers over 150000 hours of learning materials catering to diverse stages of an employees career journey.
• Flexible work model: Were 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 pleasecontactusat or 1 .

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.