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Position Overview:

The ideal candidate is highly self-driven, requires minimal oversight, and has a proven track record of translating business requirements into production-grade cloud analytics solutions. This is a hands-on technical role - you will design, build, and deliver, not only advise.

Key Responsibilities:

The contractor will own end-to-end delivery across the following areas:

Data Analytics Execution:

Design, develop, and deploy cloud-native analytics solutions supporting NALO operational KPIs (order fulfillment, inventory accuracy, throughput, No-Touch Order rates).
Build and maintain self-service dashboards and reporting layers for warehouse, logistics, and leadership stakeholders.
Translate business and operational data questions into structured analytics products with clearly defined refresh cadence, ownership, and SLAs.

Data Ingestion & Pipeline Engineering:

Architect and implement scalable data ingestion pipelines connecting SAP EWM, WMS operational data, IoT/sensor feeds, and third-party logistics platforms to cloud data platforms.
Ensure pipeline reliability, data quality validation, and lineage documentation in accordance with Lilly data governance standards.
Apply best practices for batch, micro-batch, and event-driven ingestion patterns based on source system capabilities and latency requirements.

AI & Advanced Analytics:

Prototype and deploy AI/ML use cases aligned to NALO Vision 2030 targets - including demand sensing, anomaly detection, predictive maintenance, and labor optimization.
Partner with NALO Innovation Architect to evaluate and onboard AI tooling and frameworks appropriate for distribution and logistics contexts.
Document model assumptions, limitations, and performance metrics transparently for non-technical stakeholders.

Collaboration & Program Integration:

Integrate analytics deliverables with active NALO 2.0 program workstreams - including SAP EWM, TraceLink, Tulip, and Swisslog automation tracks.
Participate in Agile sprint ceremonies, maintain delivery visibility in Jira, and proactively surface blockers without requiring escalation.
Contribute to data product governance documentation, including data dictionaries, access controls, and lifecycle review artifacts.

Required Qualifications:

Requirement Detail Education Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or a related quantitative discipline.
Experience 5+ years in data engineering, analytics, or applied AI roles; minimum 3 years in cloud analytics environments.
Cloud Platforms Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) - including managed data services (e.g., Redshift, Synapse, BigQuery, Databricks, Snowflake) Data Ingestion.
Demonstrated experience designing and operating data ingestion pipelines (ETL/ELT) using tools such as Azure Data Factory, AWS Glue, Informatica, dbt, Apache Kafka, or equivalent Analytics & BI Proficiency in SQL, Python (pandas, PySpark, or equivalent); experience delivering production dashboards in Power BI, Tableau, or similar platforms AI/ML Working knowledge of ML model development and operationalization (MLOps); experience with at least one major ML framework (scikit-learn, XGBoost, or similar).
Self-Direction Demonstrated ability to scope, prioritize, and deliver independently in ambiguous program environments without day-to-day supervision.

Preferred Qualifications:

Candidates who bring the following will be differentiated in evaluation:
Experience with SAP EWM, SAP BW/HANA, or similar ERP/WMS data environments - understanding of order, inventory, and movement data models.
Background in pharmaceutical, life sciences, or regulated manufacturing/distribution - familiarity with GxP data requirements.
Familiarity with Tulip Operations Platform, Swisslog automation systems, or TraceLink serialization platforms.
Experience implementing data mesh, data product, or domain-oriented data architecture patterns.
Knowledge of DCAM (Data Management Capability Assessment Model) or equivalent data governance frameworks.
Exposure to GenAI/LLM-based use cases in operational or enterprise settings.
Active certification in a cloud data platform (e.g., AWS Certified Data Analytics, Azure Data Engineer Associate, Google Professional Data Engineer, Databricks Certified).

What Success Looks Like:

This is not a staff augmentation role. The right candidate will operate as a peer contributor within the NALO Innovation team - bringing their own judgment on technical architecture, proactively identifying gaps in the data strategy, and delivering against program milestones with accountability. Within 90 days, the contractor should be able to:

Have an active data ingestion pipeline connected to at least one NALO operational data source in the cloud environment.
Deliver a functional analytics product (dashboard or model output) consumed by a NALO stakeholder.
Produce a documented data product brief for at least one NALO KPI domain.
Operate independently within the Agile delivery model, maintaining Jira hygiene and surfacing delivery risk proactively.

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