Senior Data Engineer, IT Enterprise Data Solutions

Bill & Melinda Gates Foundation•Seattle, WA

2d•$173,100 - $259,700•Onsite

About The Position

As part of the IT Enterprise Data Solutions (EDS) department, the Data Engineering team’s mission is to lead on data technology and utilization, enabling informed decision-making and strategic insights across the foundation. We are committed to providing a robust, secure, and scalable data platform that empowers data-driven initiatives and cultivate collaboration. Working with our business operations and foundation strategy program partners, the team supports the management of the EDW, Enterprise Data Platform, building and integration of data systems, shared data exchange, data platform operations and data architecture support. As a Senior Data Engineer, you will design, build, operate, and continuously improve secure, scalable, and reliable data solutions on the foundation’s Modern Data Platform. You will be a senior hands-on engineer and domain expert who translates business and technical requirements into production-ready pipelines, data products, models, and platform capabilities. You will work with data and AI engineers, business systems analysts, product owners, data analysts, BI engineers, security specialists, and service partners. Your work will improve the availability, quality, lineage, and usability of data for reporting, search, AI-assisted knowledge discovery, retrieval-augmented generation, and other data-driven decision-making across the foundation and its affiliates. This is a Seattle based role.

Requirements

Bachelor’s degree in computer science, data engineering, information systems, or a related field, or equivalent combination of education and experience.
Typically 7+ years of relevant data engineering experience in enterprise environments, including designing, delivering, and operating production data platforms and pipelines.
Strong hands-on expertise with Azure data services, Databricks, Snowflake, and dbt Cloud; experience with Azure Data Factory and Fivetran is strongly preferred.
Advanced proficiency in SQL and Python, plus practical experience with Spark/PySpark or Scala and cloud scripting or automation.
Experience implementing lakehouse, cloud data warehouse, ELT/ETL, and medallion or layered data architecture patterns.
Demonstrated experience with dimensional modeling, data normalization, slowly changing dimensions, point-in-time history, and enterprise data quality practices.
Experience building CI/CD pipelines for data workloads, automated testing, infrastructure-as-code or configuration-as-code, and modern version control workflows.
Strong understanding of data security, privacy, access controls, encryption, lineage, retention, and governance in cloud data environments.
Experience supporting analytics and semantic-layer platforms such as Power BI, and working with metadata/governance platforms such as Collibra, is preferred.
Demonstrated ability to enable AI through data, including an understanding of the architecture, quality, metadata, permissions, lineage, scalability, and evaluation requirements for search, RAG, and other AI use cases.
Proven ability to diagnose production issues, lead technical problem solving, and implement short-, medium-, and long-term corrective actions.
Experience working with vendors or distributed engineering teams and reviewing deliverables for alignment with enterprise standards and maintainability.
Excellent written and verbal communication skills, sound judgment, and the ability to explain complex technical concepts to varied audiences.
A demonstrated commitment to diversity, equity, inclusion, and respectful collaboration.
Must have unrestricted work authorization in the country where this position is located.
The Foundation does not provide immigration-related sponsorship for this role. This includes direct company sponsorship and any work authorization requiring a written submission or other immigration support from the company (eg: H-1B, O-1, L-1, E, OPT, STEM-OPT, CPT, TN, J-1, etc.).

Nice To Haves

experience with Azure Data Factory and Fivetran is strongly preferred.
Experience supporting analytics and semantic-layer platforms such as Power BI, and working with metadata/governance platforms such as Collibra, is preferred.

Responsibilities

Design, develop, test, deploy, and support configuration-driven ELT/ETL pipelines that acquire data from enterprise applications, databases, APIs, file repositories, partner data sources, and shared data exchanges.
Build reusable ingestion and transformation patterns for structured, semi-structured and unstructured data, including relational data, JSON, XML, CSV, Excel, and document metadata.
Develop curated data products and dimensional models using star and snowflake techniques to support enterprise reporting, semantic models, analytics, and downstream operational use cases.
Implement transformations using SQL, Python, Spark/PySpark, Databricks notebooks, dbt Cloud, Snowpark, and related cloud-native engineering tools.
Contribute hands-on to modernization initiatives, including migration from legacy data warehouse and ETL patterns to lakehouse, cloud warehouse, and ELT-based architectures.
Monitor and optimize pipelines, storage, compute, and databases for performance, scalability, availability, and cost efficiency.
Implement data quality controls, reconciliation, exception handling, observability, logging, alerting, and service-level measures for critical data flows.
Lead or support incident triage, root-cause analysis, restoration, and follow-through on preventive actions for production data services.
Apply secure engineering practices including managed identity, role-based access, encryption, secrets management, least-privilege access, and sensitive-data handling requirements.
Implement and maintain CI/CD, automated testing, version control, release management, and environment promotion practices for data workloads.
Engineer governed, high-quality data and document pipelines that support enterprise search, AI-assisted knowledge discovery, retrieval-augmented generation, analytics, and data science use cases.
Prepare and curate source data, metadata, permissions, lineage, and quality signals needed for reliable AI solutions while preserving source-system security and access policies.
Partner with Knowledge Management, AI, Business Intelligence, and Information Security teams to define fit-for-purpose data products for model grounding, evaluation, and responsible use.
Help establish repeatable patterns for document ingestion, chunking-ready content preparation, semantic metadata, and controlled data exchange across internal teams and affiliates.
Evaluate emerging data and AI platform capabilities through proofs of concept and recommend adoption based on value, security, sustainability, and architectural fit.
Conduct design and code reviews, contribute to engineering standards, and promote reusable patterns for data modeling, orchestration, testing, observability, and documentation.
Collaborate with architects and product teams to clarify requirements, assess trade-offs, estimate delivery, and convert solution designs into actionable implementation plans.
Mentor data engineers and contingent staff through pairing, technical guidance, review, and knowledge sharing; may coordinate work across small delivery efforts.
Create and maintain technical documentation, runbooks, data mappings, lineage, and operational procedures; contribute metadata and descriptions for synchronization with Collibra.
Work effectively in Agile delivery teams using Scrum, Jira, and DevSecOps practices while communicating clearly with technical and non-technical partners.