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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Mid-Level Business Intelligence Analyst - **Company:** Boeing Company - **Location:** Dallas, TX, United States - **Experience:** Experienced - **Salary:** $105,400.0 - $152,950.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Business Analytics Applications, Data Analysis, Microsoft Azure, BigQuery, Cloud Storage, Information Systems, Data Cleansing, Information Engineering, Extract Transform Load (ETL), Data Transformation, Data Visualization, Relational Databases, DevOps, Data Flow Control, Python (Programming Language), Machine Learning, Microsoft SQL Server, Oracle (Applications), Scrum Methodology, Power BI, Cloud Services, Cloudera, SQL Databases, Data Streaming, Systems Integration, Tableau (Software), Teradata SQL, Management of Software Versions, Data Processing, Google Cloud, Data Ingestion, Large Language Models, Prompt Engineering, Generative AI, Core Data, Information Technology, Statistics Packages, Data Management, Machine Learning Operations, Serverless Computing, Amazon Redshift, Microservices - **Published:** June 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=bd8003ebc53d5ab3 ## About the Role Do you have experience in Statistical analysis tools?, Do you have a Master's degree?, * 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, * Master's degree in computer science, Information Systems, Data Science, Business Analytics, or related field * Experience with 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) ## Description Partners in the design of Business Intelligence information models, dashboards, metrics and reports using standard technologies and methodologies that meet business objectives. Combines 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. This role is primarily a BI Analyst role but 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., * 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 ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [Got AI ideas but no money? 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