Data Engineer Jobs

About The Position

Requirements

• 15+ years of software or data engineering experience, or 13+ years with a Master’s degree.
• Proven track record designing, building, and operating distributed data platforms at enterprise scale.
• Deep experience designing horizontal, multi-tenant platform services used by other engineering teams.
• Understanding of distributed-system failure modes, eventual consistency, and tradeoffs among latency, availability, and cost.
• Practical experience building or managing data trust stack components, such as data contracts, metadata catalogs, data quality frameworks, end-to-end lineage, and fine-grained access control (RBAC/ABAC).
• Advanced knowledge of modern data ecosystems, specifically Snowflake, Kafka or similar streaming platforms, and AWS data services.
• Ability to govern data in motion as effectively as data at rest.
• Mastery of one or more core programming languages, such as Python, Java, or Go, and advanced SQL.
• Comfort writing production-grade code, conducting rigorous code reviews, and building declarative infrastructure using tools such as Terraform and CI/CD.
• History of driving multi-team, cross-functional technical initiatives and building consensus across Engineering, Security, Privacy, and Product teams.
• Ability to translate compliance and risk requirements into engineering primitives.
• Experience managing Tier-0 or mission-critical infrastructure.
• Trust-but-verify operational approach, including automated testing, drift detection, and circuit breakers rather than manual processes.

Nice To Haves

• Experience operating in a highly regulated environment where auditability, data privacy (CCPA/GDPR), and data integrity are architectural requirements.
• Experience building semantic layers, knowledge graphs, or context engines that ground LLMs and autonomous agents in governed enterprise data.
• Contributions to relevant open-source data or metadata projects, such as Apache Iceberg, OpenMetadata, DataHub, or Amundsen.

Responsibilities

• Define and execute technical direction for data contracts, metadata and catalog modernization, lineage, data quality, privacy, and access controls.
• Translate business, regulatory, and engineering requirements into a coherent platform strategy and sequence investments for long-term impact.
• Build platform services, libraries, and reference patterns that make contracts, quality rules, privacy policies, and access requirements versioned, testable, observable, and automatically enforceable.
• Establish technical foundations for a governed semantic context layer connecting business definitions, metadata, lineage, ownership, quality, freshness, and access policy.
• Enable trusted discovery, analytics, and AI-assisted or agentic experiences without making the role a model-development function.
• Lead the evolution of Data Trust capabilities across Snowflake, AWS data services, Kafka-based ingestion, and other batch or low-latency environments.
• Establish explicit SLOs, failure modes, correction paths, and operational ownership, supported by automated detection, containment, remediation, and recovery.
• Improve production data systems through observability, integrity checks, audit evidence, incident learning, and safe automation.
• Drive multi-quarter initiatives across teams and functions and build alignment on architectural tradeoffs and investment decisions.
• Serve as a technical authority for Data Trust, mentor senior engineers, strengthen design and implementation practices, and raise engineering standards.
• Contribute hands-on code, prototypes, architecture, and operational improvements.
• Develop reusable enterprise capabilities and reference patterns that partner teams can adopt.