Lead Systems and Data Analyst

Boeing•Tukwila, WA

1d•$148,750 - $215,050•Hybrid

About The Position

The Boeing Company is currently seeking a Lead Systems and Data Analyst to join the team in Tukwila, WA; Everett, WA; Mesa, AZ; Plano, TX; Renton, WA; Ridley Park, PA or Seattle, WA. We are seeking a proactive Data Steward to serve as the single point of contact between business stakeholders and engineering teams. The ideal candidate will gather and translate business requirements, ensure data is well-defined, accessible, and fit for purpose, and partner closely with engineering to design, develop, test, and deploy data solutions. A deep understanding of end-to-end process flows, data lifecycle, governance, and data quality management is essential.

Requirements

10+ years of experience as a data or business analyst
10+ years of experience leading Data and Artificial Intelligence (AI) Governance Programs
10+ years of experience in Artificial Intelligence (AI) governance, data governance, or related governance / compliance roles
10+ years of experience with data governance frameworks and compliance standards relevant to data management
10+ years of experience partnering directly with product, platform, engineering, or technical operations teams to implement governance within delivery environments
10+ years of experience with integrating export controls, sanctions, or government contract requirements (ITAR/EAR/CDRL) into data governance
10+ years of experience working with Data Quality or Data Cleansing activities
10+ years of experience demonstrating exceptional business, analytical, and problem-solving skills
Must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a “U.S. Person” as defined by 22 C.F.R. §120.62 is required. “U.S. Person” includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee.

Nice To Haves

Excellent communication skills; able to present complex data topics to business and technical audiences
Proficiency with SQL for data exploration and validation
Experience with data catalog/metadata tools (e.g., Collibra, Alation, Informatica)
Experience with data integration and orchestration tools (e.g., Airflow, Talend, DBT)
Experience with cloud data platforms (e.g., AWS, Azure, GCP) and data warehousing technologies (Snowflake, Redshift, BigQuery)
Knowledge of data modeling concepts (conceptual, logical, physical) and MDM principles
Experience with data quality tooling (e.g., Great Expectations, Deequ, Talend Data Quality)
Certifications in data governance, data management, or cloud platforms are a plus

Responsibilities

Act as the single point of contact for assigned data domains; build and maintain strong relationships with business stakeholders and engineering teams
Elicit, document, and prioritize business requirements related to data definitions, access, transformations, and reporting needs
Translate business requirements into technical specifications and acceptance criteria for engineering
Partner with engineering and data platform teams to design, develop, test, and deploy data solutions
Define and maintain data definitions, business glossary, canonical records, and metadata to ensure consistent use across the organization
Own data access processes: manage access requests, role-based access controls, and ensure compliance with security and privacy policies
Implement and monitor data quality rules, perform profiling, triage issues, and coordinate corrective actions with data owners and engineering
Map and maintain data lineage and end-to-end process flow documentation to support traceability and impact analysis
Establish Key Performance Indicators (KPIs) and Service Level Agreements (SLAs) for data quality, availability, and timeliness; report status and trends to stakeholders and leadership
Facilitate data governance forums, stewardship councils, and working groups to drive standards, policies, and issue resolution
Support onboarding of new data consumers by providing training, documentation, and self-service resources (data catalogs, playbooks)
Advocate for continuous improvement in data processes, automation, and observability