Senior Data Engineer, Business Operations

SK Life Science • Paramus, NJ

19h

About The Position

The Senior Data Engineer, Biz Ops will play a critical role in architecting the data infrastructure powering our AI-driven business operations platform. This role is responsible for analyzing the current database environment, redesigning it for AI-native use cases, and establishing the foundational data architecture for our end-to-end decision-intelligence platform-driven business operations platform. This role is responsible for analyzing the current database environment, redesigning it for AI-native use cases, and establishing the foundational data architecture for our end-to-end decision-intelligence platform.‑driven business operations platform. This role is responsible for analyzing the current database environment, redesigning it for AI‑native use cases, and establishing the foundational data architecture for our end‑to‑end decision‑intelligence platform. You will design scalable data ecosystems—including Data Lakes, Data Pipelines, and semantic modeling layers—using modern engineering standards (dbt, orchestration frameworks, CI/CD). Working closely with commercial and business operations experts, you will dissect existing workflows and reimagine them as AI-ready, streamlined processes in collaboration with AI Scientists and AI Engineers-ready, streamlined processes in collaboration with AI Scientists and AI Engineers.‑ready, streamlined processes in collaboration with AI Scientists and AI Engineers. You will translate ambiguous operational and business challenges into clean, reliable, ontology-aligned data models that enable forecasting, planning, and optimization across the value chain, beginning with supply chain operations. This is a high-impact senior role for someone who thrives in owning a data ecosystem end-to-end and building AI-centric data infrastructure from the ground up-aligned data models that enable forecasting, planning, and optimization across the value chain, beginning with supply chain operations. This is a high-impact senior role for someone who thrives in owning a data ecosystem end-to-end and building AI-centric data infrastructure from the ground up.‑aligned data models that enable forecasting, planning, and optimization across the value chain, beginning with supply chain operations. This is a high‑impact senior role for someone who thrives in owning a data ecosystem end‑to‑end and building AI‑centric data infrastructure from the ground up. Someone who enjoys owning a data ecosystem end ‑ to ‑ end and building from zero to one. A strategic thinker who balances strong technical depth with understanding of real business context. An engineer who thrives in close collaboration with Commercial and AI teams to define how data powers decisions. A builder comfortable operating in a fast ‑ paced, startup ‑ like environment where innovation and speed matter. An “Agile Operator” who can rapidly prototype for PoCs while architecting for long ‑ term scalability and reliability.

Requirements

• Bachelor’s degree or higher in Computer Science, Engineering, or related field.
• 5+ years of hands-on experience in Data Engineering or technical Analytics Engineering, with deep experience building data lakes and orchestrating complex pipelines.
• Strong programming proficiency in Python and PySpark for large‑scale distributed data processing, data manipulation, automation, and pipeline development.
• Expert‑level SQL for data modeling, complex transformations, and performance optimization.
• Experience with modern data lake table formats such as Apache Iceberg.
• Familiarity with Medallion Data Architecture (Bronze/Silver/Gold) for scalable and governed data processing.
• Hands‑on experience with modern transformation frameworks (e.g., dbt) and orchestration tools (e.g., Airflow or Python-based schedulers).
• Knowledge of core AWS or Azure data services and data observability practices.
• Experience optimizing data models for BI and visualization tools (e.g., Tableau).
• Ability to define business metrics and derive semantic meaning from operational KPIs.

Nice To Haves

• Master’s degree or higher in a quantitative or technical field.
• Experience working with ML pipelines (e.g., MLflow, Feature Stores) and collaborating with AI Scientists/Engineers.
• Knowledge of ontology‑based modeling, semantic layers, and modern data architectures (e.g., Data Mesh, Data Fabric).
• Experience with Graph Databases (e.g., Neo4j) for semantic modeling, ontology alignment, or operational knowledge graphs.
• Domain experience in Supply Chain Management (SCM), BizOps, RevOps, or Commercial Operations.
• Experience in regulated industries (e.g., biopharma, healthcare, finance).
• Experience in a BizOps or highly cross‑functional technical role.
• Hands‑on experience with Snowflake architecture.

Responsibilities

• Analyze existing databases and redesign them for AI/ML readiness, including ontology ‑ driven and semantic data modeling.
• Architect and implement centralized Data Lake and scalable, robust data pipelines supporting operational workflows and AI ‑ driven decision processes.
• Build and maintain high ‑ quality data transformations using dbt and enforce software engineering best practices across the data stack.
• Design feature ‑ ready data models to support AI/ML use cases such as forecasting, classification, and optimization.
• Develop secure and reliable data ingestion frameworks (batch and streaming) with strong observability and performance controls.
• Partner with Commercial, Marketing, and AI teams to translate business problems into data requirements, semantic models, and scalable pipelines.
• Implement data quality, lineage, and governance practices aligned with enterprise standards.
• Lead technical direction on modern data stack architecture and continuously improve scalability, efficiency, and maintainability.
• Contribute to an agile, experimentation ‑ driven culture, balancing rapid PoC execution with long ‑ term architectural integrity.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level