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.