(IND) STAFF, DATA ENGINEER

About the role

Position Summary...

What you'll do...

About Team

The Data Engineering team at Global Data Foundations builds and operates trusted, scalable data platforms and products that enable analytics, reporting, machine learning, and operational decision-making across the enterprise. We partner with Product, Engineering, Analytics, Data Science, and business teams to transform complex data into reliable, governed, and reusable assets. Our work spans high-volume batch and streaming pipelines, cloud data platforms, data modeling, observability, governance, and platform modernization at global scale.

As a Staff Data Engineer, you will provide hands-on technical leadership across multiple initiatives. You will shape architecture, solve ambiguous data problems, establish engineering standards, and influence teams to deliver secure, resilient, cost-effective data solutions that create measurable business value.

What you'll do

· Lead the architecture, design, and delivery of large-scale data platforms, pipelines, and products supporting complex business capabilities across multiple domains.

· Translate ambiguous business and operational problems into well-defined data solutions, technical roadmaps, executable milestones, and measurable outcomes.

· Drive the execution of multiple business plans and projects by identifying customer and operational needs, communicating priorities, removing technical barriers, managing dependencies, and adapting plans based on progress and risk.

· Build and optimize reliable batch and streaming data pipelines using modern ETL/ELT, API, event-driven, and distributed-processing patterns.

· Design scalable conceptual, logical, and physical data models across relational, dimensional, NoSQL, data lake, and warehouse platforms.

· Identify authoritative data sources, define fit-for-purpose ingestion strategies, and establish data-quality checks, service-level objectives, lineage, and reconciliation controls.

· Provide technical direction through architecture reviews, design sessions, code reviews, proof-of-concepts, and production-readiness assessments.

· Develop and review production-quality code using SQL, Python, Java, or equivalent languages; establish robust testing, CI/CD, and release automation practices.

· Improve platform reliability through observability, capacity planning, performance tuning, incident prevention, root-cause analysis, and automated remediation.

· Partner with security, privacy, legal, governance, infrastructure, and product teams to ensure data is handled ethically and complies with enterprise and regulatory requirements.

· Define and champion engineering standards for data modeling, metadata, documentation, reusable frameworks, scalability, security, and cost efficiency.

· Mentor engineers, raise the technical bar, cultivate a learning environment, and influence teams without relying on direct authority.