Senior Data Engineer

Senior Data Engineer

Snowflake, Semantic Layer & Enterprise Data Platform

Mustang Cat is seeking a highly experienced Senior Data Engineer to design, own, and govern the enterprise data environment, with a strong focus on Snowflake, semantic layer design, trusted reporting, and enterprise data modeling.

This role is responsible for ensuring that data is structured, transformed, secured, documented, and made available in a way that supports accurate operational and financial reporting across the business. The Senior Data Engineer will act as the technical steward of the enterprise data platform, ensuring that data models, reporting layers, metric definitions, semantic structures, and data quality standards are intentionally designed and consistently applied.

The successful candidate must be deeply experienced in Snowflake, advanced SQL, dimensional modeling, analytics-ready data structures, and semantic layer design. This role will work closely with business stakeholders, reporting teams, application owners, integration engineers, and external vendors to ensure that the data warehouse becomes a trusted foundation for reporting, analytics, and future advanced use cases.

While this role will collaborate with engineers responsible for ingestion, integrations, and data movement, the Senior Data Engineer will focus primarily on data architecture, modeling, semantic layer design, reporting-layer reliability, governance, and platform stewardship.

Key Responsibilities:

Snowflake Data Platform Ownership & Design

Own the design and evolution of the enterprise data platform, including:

• Snowflake data warehouse architecture
• Raw, staging, curated, semantic, and reporting-ready layers
• Schema and object design
• Data models, views, tables, procedures, and transformation logic
• Storage, compute, and performance optimization strategies
• Define how data should be organized, modeled, and accessed to support reporting, analytics, operational decision-making, and downstream use cases.
• Ensure the Snowflake environment is designed for scalability, cost efficiency, performance, security, and long-term maintainability.
• Establish and maintain standards for Snowflake object naming, schema organization, access control, environment separation, and change management.

Data Modeling & Semantic Layer Design
Design and maintain core data models that represent business entities, metrics, relationships, and reporting structures.

Define conformed dimensions, fact tables, shared reference data, hierarchies, and reusable reporting models to ensure consistency across analytics and reporting.

Establish and maintain semantic layer standards so business definitions, calculations, KPIs, and measures are applied consistently across tools and teams.

Partner with reporting and analytics teams to ensure models align with how data is consumed in Power BI and other reporting tools.

Help move critical reporting logic out of isolated reports, spreadsheets, and disconnected BI models into governed Snowflake and semantic-layer structures where appropriate.

Ensure business metrics are defined once, documented clearly, and used consistently across the enterprise.

Data Quality, Trust & Governance
Define data quality standards, validation frameworks, reconciliation logic, and exception reporting expectations.

Identify upstream data quality issues, transformation gaps, integration problems, and reporting inconsistencies that affect trust in enterprise reporting.

Establish ownership and accountability for critical data domains, business definitions, and reporting metrics.

Ensure data lineage, freshness, completeness, accuracy, and reliability expectations are clearly defined and measurable.

Partner with integration and data movement resources to ensure pipelines conform to data quality, modeling, and governance standards.

Support the validation of key operational and financial metrics from source systems through Snowflake and into reporting outputs.

Environment Management & Standards
Define and maintain standards for:

• Data environments, including development, test, and production
• Snowflake schemas and database objects
• Naming conventions
• Access control and role-based permissions
• Transformation logic and reusable patterns
• Deployment and change management practices
• Documentation and data model stewardship

Ensure the data environment aligns with security, compliance, audit, and operational requirements.

Act as a gatekeeper for changes that materially affect data structure, semantic definitions, reporting logic, or data integrity.

Collaboration with Integration & Analytics Teams
Provide clear architectural guidance to integration engineers, data movement resources, reporting analysts, and external vendors.

Review and approve data structures, transformations, and reporting-layer changes implemented by internal teams or vendor partners.

Partner with analytics, Power BI, and business reporting teams to ensure data is usable, performant, accurate, and well-documented.

Serve as an escalation point for complex Snowflake, data modeling, semantic layer, metric definition, and platform design decisions.

Translate business reporting requirements into clear data structures, models, and reusable reporting objects.

Documentation & Knowledge Stewardship
Produce and maintain clear documentation covering:

• Snowflake architecture and platform design
• Data models, facts, dimensions, and reporting structures
• Semantic layer definitions
• Business metric definitions
• Transformation logic and business rules
• Data lineage and source-system dependencies
• Standards, guardrails, and best practices
• Ensure documentation supports long-term ownership, reduces institutional knowledge risk, and enables consistent use of enterprise data across teams.

Required Qualifications:

• 8-12+ years of experience in data engineering, analytics engineering, data architecture, or enterprise data platform roles.
• Strong hands-on experience with Snowflake.
• Demonstrated experience designing and owning enterprise data environments.
• Advanced SQL expertise, including complex queries, performance tuning, analytical functions, stored procedures, and transformation logic.
• Strong experience with data warehouse design and optimization.
• Strong experience with dimensional and analytical data modeling.
• Experience designing semantic layers, reporting layers, data marts, or governed enterprise metric models.
• Strong understanding of facts, dimensions, hierarchies, conformed entities, reference data, and reusable reporting structures.
• Experience supporting enterprise reporting environments, preferably including Power BI or similar BI tools.
• Strong understanding of ETL/ELT concepts, data lineage, access control, and data quality frameworks.
• Ability to understand complex source systems, including ERP, CRM, operational platforms, and legacy databases.
• Ability to lead data design decisions without being limited to pipeline execution.
• Strong communication skills, with the ability to explain data structures, standards, and technical decisions to both technical and non-technical audiences.

Preferred Qualifications:

• Deep experience with Snowflake performance optimization, cost management, role-based access control, and environment design.
• Experience defining semantic layers and enterprise metric catalogs.
• Experience with Power BI semantic models, tabular models, certified datasets, or governed BI data models.
• Familiarity with data orchestration and ingestion patterns, including tools such as Azure Data Factory, dbt, Fivetran, Matillion, Talend, Informatica, Boomi, or similar platforms.
• Experience with ERP, Salesforce/CRM, financial systems, operational reporting, or legacy system environments.
• Experience operating data platforms in regulated, audit-sensitive, or control-focused environments.
• Experience mentoring engineers, analytics engineers, or reporting developers.
• Familiarity with AI, machine learning, or advanced analytics use cases supported by governed enterprise data.

What Success Looks Like:

The Snowflake environment is clearly structured, documented, and consistently implemented.

Business metrics, reporting definitions, and semantic models are defined once and used consistently across analytics and reporting.

Core facts, dimensions, hierarchies, and business entities are reusable, trusted, and aligned with business needs.

Data quality issues are detected early and addressed systematically.

Reporting teams consume trusted, well-modeled datasets rather than rebuilding logic in individual reports or spreadsheets.

Integration pipelines conform to defined data standards, contracts, and quality expectations.

Power BI and other reporting tools are supported by governed data models and reliable semantic structures.

The data platform scales without fragmentation, uncontrolled complexity, or inconsistent reporting logic.

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Mustang Cat is an Equal Opportunity Employer.