Mid-Level Business Intelligence Analyst

Boeing•Seattle, WA

2d•Hybrid

About The Position

The Boeing Company is seeking a Mid-Level Business Intelligence Analyst to join their team. This role involves partnering in the design of Business Intelligence information models, dashboards, metrics, and reports using standard technologies and methodologies to meet business objectives. The analyst will combine SQL, Business Intelligence (BI) best practices, cloud data engineering awareness, and domain knowledge to deliver efficient and scalable BI solutions that improve productivity and business outcomes. While primarily a BI Analyst role, it requires practical familiarity with Generative Artificial Intelligence (GenAI) and prompt engineering techniques to accelerate analytics, automate workflows, and prototype Large Language Model (LLM)-enabled features.

Requirements

5+ years of experience in Business Intelligence, Analytics, or Data Engineering roles
Experience with SQL and relational databases (Oracle, Teradata, SQL Server)
Experience working with cloud platforms (GCP and/or AWS)
Experience with core data services (storage, ETL/streaming, warehouses, serverless computing)
Experience building data products on cloud platforms (ingestion, transformation, cataloging, serving)
Experience with BI/visualization tools such as Tableau and/or Power BI and ability to design scalable dashboards and data models
Experience with Python and/or R for data preparation, analysis, and/or prototyping
Experience with data modeling, ETL patterns, data quality practices, and common BI architecture
Experience with Agile, DevOps and release methodologies

Nice To Haves

Master’s degree in computer science, Information Systems, Data Science, Business Analytics, or related field
Strong communication skills with the ability to translate business problems into analytics solutions
Experience working in supply chain, manufacturing, and/or aerospace domains
Experience with GenAI/LLM tools and APIs (e.g., OpenAI, Anthropic, Azure OpenAI, or on-prem alternatives) and prompt engineering for analytics use cases
Experience building and/or integrating LLM-powered features: natural-language-to-SQL, summary/explainability layers, intelligent alerting, and/or conversational analytics
Experience with model operationalization concepts (MLOps), versioning, monitoring, and latency/throughput tradeoffs
Experience with secure and compliant handling of data when using LLMs, including PII redaction techniques and enterprise governance practices
Experience with automation frameworks, orchestration (Airflow, Prefect), and/or microservice APIs for embedding GenAI into workflows
Experience prototyping production-ready PoCs that demonstrate measurable business value from GenAI and/or ML enhancements
Experience with scripting and tooling for prompt testing, evaluation metrics, and prompt libraries
Experience with cloud-native managed services on GCP and AWS for data products (e.g., BigQuery, Dataflow, Dataproc, Cloud Composer, S3, Redshift, Glue, Lambda)

Responsibilities

Design reusable BI information models, views, dashboards, metrics and reports that meet business needs across supply chain and related functions
Translate business requirements into technical designs, user stories and acceptance criteria; validate delivered solutions with stakeholders
Build, optimize and maintain SQL (Redshift/Oracle/Teradata/SQL Server) queries, Extract Transfer Load (ETL)/data wrangling pipelines and data transformations for reliable reporting
Produce analytics deliverables (ad-hoc analysis, trend reports, root-cause investigations) to support decision-making
Prototype and evaluate GenAI/LLM use cases to augment BI workflows (e.g., natural-language querying, automated data summarization, intelligent insights generation, prompt templates for analysts)
Apply prompt engineering best practices: design, test, and optimize prompts and chains for consistent, accurate outputs; document prompt libraries and guardrails
Integrate LLM/GenAI capabilities with BI tools or data platforms where appropriate (e.g., Application Programming Interfaces (APIs), wrappers, microservices) in collaboration with engineers and data scientists
Ensure responsible use of GenAI: implement data privacy, Personally Identifiable Information (PII) handling, output validation, and model risk mitigation practices
Collaborate with data scientists, Machine Learning (ML) engineers, and platform teams on productionizing Artificial Intelligence (AI)-enabled features and understanding model performance and limitations
Develop and deliver stakeholder-facing demos, playbooks, and training materials that show how GenAI augments BI workflows
Work with cloud data platforms (Google Cloud Platform (GCP) and Amazon Web Services (AWS)) to design, develop, and maintain data products — including data ingestion, storage, transformation, and serving layers
Build and validate data products on cloud platforms (BigQuery, Cloud Storage, Dataflow, AWS S3, Redshift, Glue, etc.) and collaborate with platform/engineering teams on deploying them to production
Participate in agile delivery: sprint planning, estimation, development, testing and release activities
Identify opportunities to improve automation, reduce analyst toil, and scale analysis via templated assets and self-service