Lead Analytics Engineer

Overview

Lead analytics engineering, building ML-ready data pipelines, feature stores, and team.

Key Responsibilities

• feature stores
• data architecture
• model registry
• feature pipelines
• data quality
• monitoring alerts

Tasks

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

Requirements

• python
• sql
• mlflow
• kubeflow
• spark
• feature stores

What You Bring

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

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Benefits

-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. -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.

About Altus Group

-Evolved from pure advisory into a global CRE technology powerhouse. -Its flagship Argus software suite underpins financial modeling, asset and portfolio management across commercial real estate clients worldwide. -The firm blends deep CRE consulting—valuation, cost advisory, debt and development services—with data-driven analytics and software. -Typical projects range from large-scale property valuations, development cost management to portfolio investment analytics for funds and lenders. -Notable moves include spinning off geomatics, acquiring Voyanta, Taliance, Reonomy, and Finance Active to build an 'intelligence as a service' platform. -Hosts client conferences (ARGUS/Altus Connect) and launched ARGUS Intelligence in 2024. -Maintains a presence in 16 countries, integrating software, data, and consulting into a cohesive CRE narrative.

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