Senior Software Developer - Data Engineering-2

Boeing•Seattle, WA

8d•$158,950 - $215,050•Hybrid

About The Position

Boeing Defense, Space and Security (BDS) is seeking a Senior Software Developer – Data Engineer to support our dynamic Data Engineering team in Seattle, WA. At Boeing, we are all innovators on a mission to connect, protect, explore, and inspire. From the seabed to outer space, you’ll learn and grow, contributing to work that shapes the world. Find your future with us. Our team is building an AWS-based data lakehouse in AWS GovCloud and preparing the enterprise data foundation to power Palantir Foundry and ontology-driven analytics. We are migrating data from legacy data warehouses into the Boeing AWS Gov Data Lakehouse (DLH), implementing scalable ingestion, transformation, and data quality frameworks, and enabling secure, governed, and well-modeled data products for downstream analytics, applications, and digital thread use cases across BDS. This role will be a key technical leader in designing and implementing cloud-native data pipelines, data models, and metadata/tagging strategies that make our data lake ready for Palantir ontology, while ensuring compliance with security and regulatory requirements in a highly controlled environment.

Requirements

5+ years of experience writing software for data services or data-intensive applications.
5+ years of experience with SQL development, including performance tuning and complex queries.
5+ years of experience with ETL/ELT programming and data transformation.
5+ years of experience developing data pipelines in production environments.
5+ years of experience developing software products or data platforms in a cloud environment (e.g., AWS).
5+ years of experience with AWS data and analytics services such as S3, Glue, Athena, Redshift/Redshift Spectrum, EMR, Lambda, or Step Functions.
5+ years of experience migrating data from legacy data warehouses (e.g., Teradata, Oracle, SQL Server, Netezza, etc.) into cloud-based data platforms.
This position must meet export control compliance requirements. To meet export control compliance requirements, a “U.S. Person” as defined by 22 C.F.R. §120.15 is required. “U.S. Person” includes U.S. Citizen, lawful permanent resident, refugee, or asylee.

Nice To Haves

Bachelor’s degree in a technical field (Computer Science, Engineering, Information Systems, or related); Master’s or Doctorate preferred.
Experience working in AWS GovCloud or other secure/regulated cloud environments.
Experience with Palantir Foundry, ontology modeling, or similar semantic/knowledge graph platforms.
Experience in a manufacturing, aerospace, or defense environment.
Experience in data analytics and enabling data science or BI teams.
Experience with modern data engineering tools and practices (e.g., Spark, dbt, Airflow/orchestration tools, data quality frameworks, data catalog/lineage tools).
Familiarity with data governance, data classification, and role-based access control in regulated environments.

Responsibilities

Drives the vision and technical strategy for the AWS GovCloud data lakehouse and analytics foundation, accelerating the build-out of reusable, cross-platform data components.
Conceptualizes and owns the data architecture for multiple large-scale migration and ingestion projects, evaluating design and operational cost-benefit tradeoffs.
Leads the design, development, and maintenance of complex and scalable data pipelines to ingest data from legacy data warehouses and operational systems into AWS Gov DLH (e.g., S3, Glue, EMR, Kinesis).
Designs and implements robust ETL/ELT workflows using cloud-native and open-source tools (e.g., AWS Glue, Spark, Lambda, Step Functions) to transform, standardize, and curate data for Palantir Foundry and other consumers.
Provides technical leadership and mentorship to a team of data engineers, ensuring best practices in coding, testing, observability, and data lifecycle management.
Builds cross-functional relationships with Data Scientists, Product Managers, Palantir teams, and Software Engineers to understand and deliver on data needs and ontology requirements.
Defines and manages SLAs for critical data sets and pipelines, including availability, latency, and data quality metrics.
Determines and implements security and access control models based on privacy and classification requirements, ensuring compliance with export control and other safeguards.
Architects and implements end-to-end data integration, ETL, and data processing solutions aligned with business goals and Palantir ontology/Foundry use cases.
Optimizes and tunes data pipelines and storage layouts (e.g., partitioning, file formats such as Parquet) for maximum performance, scalability, and cost efficiency.
Designs, builds, and launches curated data sets and semantic models that are ontology-ready and support a variety of analytics, reporting, and operational use cases.
Solves challenging data integration and migration problems using optimal ETL patterns, frameworks, and query techniques across heterogeneous legacy systems.
Implements and enhances data tagging, classification, and metadata strategies to support governance, lineage, and self-service discovery in the data lakehouse.
Optimizes pipelines, frameworks, and shared components to facilitate easier development and reuse of data artifacts across programs and domains.
Influences product and cross-functional teams to identify data opportunities that drive measurable impact and enable digital thread and model-based engineering initiatives.
Applies proven expertise to build high-performance, scalable data warehouses and lakehouse structures on AWS.
Designs, builds, and launches efficient and reliable data pipelines to move and transform data from on-premise and cloud sources into AWS Gov DLH.
Sources external and partner data securely, ensuring appropriate controls, encryption, and monitoring are in place.
Designs data models intelligently (e.g., dimensional, normalized, or data vault) for optimal storage, retrieval, and alignment with Palantir ontology.
Deploys inclusive data quality checks and monitoring to ensure high-quality, trusted data (e.g., validation rules, anomaly detection, reconciliation).
Optimizes existing pipelines and maintains all domain-related data pipelines, ensuring reliability and continuous improvement.
Takes ownership of the end-to-end data ecosystem for assigned domains, from ingestion through consumption and decommissioning.
Collaborates with infrastructure and DevOps teams to ensure the availability, security, and efficiency of data platforms, including CI/CD for data pipelines and infrastructure-as-code.
Drives the adoption of modern data management practices, tools, and technologies (e.g., data cataloging, lineage, observability) within the organization.