Senior Data Engineer

Data Mesh Engineer

Role Summary

The Data Mesh Engineer is responsible for designing, building, and continuously improving the processes, patterns, and tooling that enable decentralized data product ownership across the organization. This role exists in two contexts:

• Practice Team (Dedicated) — Reports directly to the Principal Data Architect. Works across all product engineering teams to define standards, build shared infrastructure, and drive adoption of data mesh patterns. Not embedded in any single team's sprint cycle.
• Embedded — Reports to their delivery team lead with a dotted-line relationship to the Principal Data Architect and the Data Mesh practice. Works within their team's sprint cycle to build pipelines, develop data products, and implement the standards and patterns established by the practice.

Regardless of context, every Data Mesh Engineer works within the same four pillars: Snowflake as the primary data platform, ELT processes and patterns (including tools like Openflow), data product development (including tools like dbt) for publication through Snowflake's Internal Marketplace, and Infrastructure as Code (IaC) via Terraform to ensure Snowflake environments are provisioned, versioned, and governed consistently.

Both contexts are critical. The practice team establishes the patterns, tooling, and guardrails; embedded engineers put them into action within their domains and feed real-world learnings back into the practice.

Core Responsibilities

Data Product Design & Development

• Build data products using dbt following the conventions established by the practice—naming, versioning, documentation, contracts, and quality expectations.
• Develop and maintain shared dbt packages, macros, and testing frameworks that accelerate how teams build and validate their own data products.
• Move data products through the full lifecycle—from domain modeling through development, testing, certification, and publication to Snowflake's Internal Marketplace.

Practice Team emphasis: Define and curate the standard patterns, templates, and conventions for data product development. Set the bar for what production-ready looks like and maintain the certification criteria for marketplace publication.

Embedded emphasis: Apply practice patterns to build domain-specific data products within the team's area of ownership. Surface gaps or friction in the standards and feed improvements back to the practice.

Snowflake Platform Patterns & Optimization

• Work within established Snowflake architecture patterns: account structure, database/schema conventions, role-based access models, and resource monitoring strategies.
• Apply patterns for warehouse sizing, query optimization, cost management, and cross-account data sharing.
• Stay current with Snowflake capabilities (e.g., dynamic tables, Snowpark, Iceberg tables, Cortex) and assess their fit within the data mesh.

Practice Team emphasis: Own the shared Snowflake architecture patterns and the Internal Marketplace publishing workflows, including listing standards, access controls, and discoverability practices. Evaluate and roll out new Snowflake capabilities across the organization.

Embedded emphasis: Implement Snowflake patterns within the team's domain. Optimize warehouse usage and query performance for the team's specific workloads. Flag edge cases or pattern gaps back to the practice.

Infrastructure as Code (Terraform)

• Provision and manage Snowflake infrastructure—databases, schemas, warehouses, roles, grants, and integrations—through version-controlled Terraform rather than manual console operations.
• Use the Snowflake-specific Terraform template to bootstrap environments with organizational standards baked in from day one.

Practice Team emphasis: Maintain and extend the Terraform codebase and onboarding template. Identify coverage gaps and progressively bring more Snowflake infrastructure under IaC management. Collaborate with platform and DevOps teams on CI/CD pipelines for Terraform plans, state management, and drift detection.

Embedded emphasis: Use the Terraform template and established modules to manage the team's Snowflake infrastructure. Contribute improvements or new modules back to the shared codebase when team-specific needs reveal gaps.

ELT Process Engineering

• Design and build ELT pipelines with a primary focus on Openflow and related tooling, following standardized patterns and frameworks.
• Instrument pipelines with monitoring, alerting, and observability so freshness and quality issues surface before downstream consumers are impacted.

Practice Team emphasis: Define the standardized ELT patterns and frameworks. Codify best practices for extraction, loading, and transformation workflows. Curate the catalog of approved connectors, patterns, and integration approaches to reduce duplication across teams.

Embedded emphasis: Build and maintain ELT pipelines for the team's domain using practice-approved patterns and connectors. Own pipeline reliability for the team's data sources and surface recurring issues or missing patterns to the practice.

Escalation Support & Hands-On Problem-Solving

• Diagnose root causes across the stack: Is it a modeling issue A Snowflake misconfiguration A Terraform drift An ELT pattern mismatch A data contract violation
• Prototype fixes, validate them, and fold the lessons back into shared patterns and documentation.

Practice Team emphasis: Act as the go-to specialist when teams hit a wall with cross-cutting issues spanning data pipelines, dbt models, Snowflake performance, Terraform configuration, or marketplace publishing. Build up a searchable knowledge base of resolved issues to reduce repeated intervention.

Embedded emphasis: Serve as the first line of troubleshooting for data issues within the team. Engage the practice team when issues are systemic or cross team boundaries.

Data Quality, Testing & Governance

• Integrate data quality testing into dbt workflows—schema tests, freshness checks, custom validations, and anomaly detection.
• Uphold data contracts that clarify expectations between producing and consuming teams.
• Track and report on data health metrics: pipeline reliability, data freshness, test pass rates, Terraform coverage, and marketplace adoption.

Practice Team emphasis: Develop the data quality testing frameworks, data contract standards, and certification criteria that determine what a data product must demonstrate before it earns a spot in the marketplace.

Embedded emphasis: Implement testing and quality standards within the team's data products. Author and honor data contracts for the team's domain. Report on data health metrics relevant to the team's area.

Knowledge Sharing & Team Enablement

• Participate in cross-team syncs to share findings, surface new patterns, and learn from other teams data product launches and infrastructure changes.
• Contribute to internal documentation, playbooks, and training materials covering dbt patterns, Snowflake usage, ELT standards, Terraform workflows, and marketplace publishing.
• Coach peers on data modeling, dbt development, Snowflake optimization, Terraform best practices, and data mesh principles.

Practice Team emphasis: Facilitate cross-team syncs and own the practice's documentation and training materials. Act as the central point of contact for tooling decisions—evaluating new tools, assessing capabilities, and recommending adoption paths.

Embedded emphasis: Champion data mesh practices within the delivery team. Bring patterns and learnings from the practice into day-to-day teamwork, and bring team-specific insights back to the practice community.

Qualifications

Required

• 5+ years of data engineering experience with strong fundamentals in data modeling, SQL, and pipeline architecture.
• Hands-on Snowflake experience spanning architecture design, performance tuning, data sharing, and role-based access control.
• Proficiency with dbt—model design, testing frameworks, package development, and documentation generation.
• Practical experience with Terraform for provisioning and managing cloud infrastructure, ideally including Snowflake resources.
• Track record of designing and maintaining ELT/ETL pipelines at scale, including orchestration, monitoring, and failure recovery.
• Ability to work through ambiguous, cross-cutting data problems that span multiple teams and technology layers.
• Strong written and verbal communication—this role depends on clear documentation, cross-team alignment, and the ability to make complex patterns approachable.

Preferred

• Experience with Openflow or similar ELT orchestration and integration tooling.
• Grounding in data mesh principles (domain ownership, data-as-a-product, self-serve infrastructure, federated governance).
• Experience publishing and managing listings in Snowflake Marketplace, including access controls and data sharing configurations.
• Familiarity with the Snowflake Terraform provider and patterns for managing complex multi-database, multi-role environments.
• Background in data quality frameworks, data contracts, or data observability tooling (e.g., Great Expectations).
• Experience influencing outcomes across multiple product teams without direct authority—leading through standards, tooling, and trust.
• Comfort with Git-based workflows for analytics code, including CI/CD for dbt projects and Terraform plans.