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Boeing · 11 hours ago

Senior Cloud Data Architect

USA - Arlington, VA

Full-time

Onsite

Senior Level

$184K/yr - $250K/yr

7+ years exp

The Boeing Company is seeking a Senior Cloud Data Architect to join their Talent Analytics and Innovations team in Arlington, VA. This role involves designing and building a scalable cloud data platform to support HR analytics and AI/ML use cases, collaborating with various teams to ensure data quality and compliance.

AerospaceIndustrial

No H1B

Responsibilities

Design and own the end-to-end cloud data platform architecture for HR data (ingest, storage, processing, serving, cataloging, and archival)

Translate HR analytics and ML requirements into logical and physical data models, data products, and platform services

Define and implement data ingestion strategies (batch, streaming), transformation patterns (ELT/ETL), and orchestration for HR sources (HRIS, payroll, LMS, recruiting, time & attendance, benefits, performance systems)

Create robust data models and schemas optimized for analytics, reporting, and ML feature stores; promote use of canonical master records and single source of truth

Implement data governance enablers: metadata management, data catalog, data lineage, data quality frameworks, access controls, encryption, and retention policies in coordination with Data Steward and Privacy teams

Design and operationalize feature stores and model-serving patterns to support HR ML initiatives (training data, feature engineering, versioning)

Establish CI/CD pipelines, infrastructure-as-code, automated testing, and deployment practices for data platform components

Collaborate with Data Science and Analytics teams to enable reproducible experiments, model registry integration, and monitoring of model performance and data drift

Ensure platform security and compliance (role-based access, masking, PII handling, anonymization/de-identification) per GDPR, CCPA, and company policies

Define SLAs, observability (metrics, logging, alerts), and cost-management practices for data pipelines and cloud resources

Mentor engineers and create platform documentation, runbooks, onboarding guides, and best-practice patterns for HR stakeholders

Participate in vendor selection, proof-of-concepts, and technology evaluations for cloud data tooling

Qualification

Cloud data architectureData engineeringData governanceMachine learning pipelinesPublic cloud platformsData modelingData ingestion strategiesETL/ELT designCI/CD pipelinesCollaborationCommunicationMentoringProblem-solving

Required

7+ years of data engineering/architecture experience, including 3+ years designing cloud data platforms

Experience supporting HR, People Analytics, or enterprise analytics use-cases

Preferred

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field

Certifications in cloud platforms (AWS Certified Solutions Architect, Azure Data Engineer, GCP Professional Data Engineer) or relevant architecture/data engineering credentials

Experience implementing feature stores and production ML data pipelines in enterprise environments

Experience working with HR systems (Workday, SuccessFactors, ADP) or People Analytics teams

Experience with public cloud platforms (AWS, Azure, or GCP) and core services for data engineering (storage, compute, serverless, IAM)

Experience with data lakehouse or modern data platform architectures (e.g., Delta Lake, Databricks, Snowflake, BigQuery, Synapse)

Experience in data ingestion and processing frameworks (Spark, Dataproc, Glue, Dataflow, Flink, Kafka, Kinesis)

Experience with ETL/ELT design, orchestration tools (Airflow, Prefect, Step Functions), and workflow automation

Experience with data cataloging and governance tools (Collibra, Alation, AWS Glue Data Catalog, Purview) and data quality tooling (Great Expectations, Deequ, Informatica)

Experience designing feature stores or enabling feature management (Feast, custom feature store patterns)

Experience with scripting including SQL, Python/Scala; infrastructure-as-code experience (Terraform, ARM, CloudFormation)

Experience with CI/CD for data applications, containerization (Docker, Kubernetes), and observability stacks (Prometheus, Grafana, Cloud monitoring)

Experience with data security controls, PII handling, anonymization techniques, and regulatory compliance for HR data