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United States
Remote only
Not Available
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director, Analytics Engineering (2 Openings) based in the United States.
This leadership role is focused on building the next generation of AI-powered data infrastructure for enterprise analytics and data science.
You will lead the development of intelligent, scalable data pipelines, feature stores, and analytics-ready repositories that support machine learning at scale.
The role combines strategic technology leadership with hands-on innovation across modern data engineering, AI, and machine learning platforms.
You will empower data scientists and analysts through self-service capabilities that make high-quality data easier to discover, access, and reuse.
A major focus will be automating data quality, anomaly detection, feature engineering, observability, and remediation across complex data environments.
You will collaborate closely with enterprise technology teams while establishing reliable, governed, and compliant platforms for sensitive data.
With two openings available, this is an opportunity to shape large-scale analytics infrastructure and accelerate AI-driven decision-making in a highly sophisticated environment.
Design and implement intelligent, self-healing data pipelines that use AI and machine learning to automate data quality monitoring, anomaly detection, and remediation.
Build and maintain centralized feature stores that enable reusable, consistent features across multiple machine learning models and analytical use cases.
Develop curated data repositories optimized for data science and AI workflows, including training datasets, evaluation datasets, and production-serving layers.
Create automated feature engineering pipelines that transform raw data into analytics-ready features while maintaining data lineage and traceability.
Partner with enterprise technology teams to optimize analytics platform architecture for high-performance data science and machine learning workloads.
Build scalable pipelines integrating diverse data sources, including sales, CRM, patient claims, real-world evidence, and unstructured data.
Develop self-service data access layers that enable data scientists, analysts, and other users to independently discover, query, and extract trusted data.
Establish and manage service-level expectations for data availability, freshness, reliability, and quality.
Implement robust monitoring, observability, and governance capabilities across data engineering and analytics infrastructure.
Lead teams responsible for enterprise-scale data platforms, feature stores, and analytics engineering capabilities.
Drive the adoption of modern AI and automation technologies while balancing innovation with production stability, reliability, security, and scalability.
Promote reusable, self-service solutions that reduce dependency on centralized engineering teams and accelerate model development and deployment.
Advanced degree in Computer Science, Data Engineering, or a closely related technical discipline.
7+ years of professional experience in data engineering, machine learning engineering, AI engineering, analytics infrastructure, or a related field.
5+ years of experience leading teams responsible for enterprise-scale data platforms, analytics infrastructure, or feature stores.
Expert knowledge of feature store technologies such as Feast, Tecton, SageMaker Feature Store, or Databricks Feature Store.
Deep expertise with modern data platforms designed for machine learning and analytics workloads, including Databricks, AutoML, Snowflake, and BigQuery.
Strong programming and data-processing skills in Python, SQL, Spark, and PySpark.
Experience with data orchestration technologies such as Airflow, Prefect, and dbt, as well as CI/CD practices for data pipelines.
Strong understanding of data governance, privacy, security, and regulatory compliance, including frameworks such as HIPAA and GDPR.
Demonstrated success implementing AI- and ML-powered automation within data engineering workflows.
Strategic mindset with the ability to balance emerging technologies and innovation with operational reliability and production stability.
Builder-oriented approach with a proven ability to create scalable, self-service data capabilities.
Experience in pharmaceutical, healthcare, life sciences, or another highly regulated industry is preferred.
Knowledge of streaming technologies, MLOps platforms, and modern data lakehouse architectures is advantageous.
Strong leadership, communication, stakeholder management, mentoring, and change management capabilities.
Ability to work effectively across technical teams and influence enterprise-level data and analytics strategy.
Annual base salary of $194,600–$361,400, with final compensation determined by skills, experience, location, education, and other relevant factors.
Performance-based cash incentive opportunity.
Eligibility for annual equity awards depending on role level and applicable criteria.
Comprehensive health benefits.
Life and disability insurance coverage.
401(k) plan with company contribution and matching.
Generous paid time off, including vacation, personal days, holidays, and other applicable leave.
Fully remote work opportunity within the United States, subject to applicable location restrictions.
Approximately 20% travel, with domestic and/or international travel expectations determined by the hiring manager.
Opportunity to lead innovative AI, machine learning, and analytics engineering initiatives at enterprise scale.
Exposure to advanced data platforms, feature engineering technologies, and AI-powered automation.
Leadership opportunity focused on building scalable infrastructure that directly enables data science and analytics innovation.