Senior Data Engineer

Mustang Cat - Houston, 12800 Northwest Freeway, Houston, Texas, United States of America Job Description Mustang Cat is a leading Caterpillar® dealer serving the construction, oil & gas, power generation, marine, and manufacturing industries in Southeast Texas. We are looking for a senior data engineer to design, govern, and own our enterprise data environment with a focus on Snowflake. 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 optimisation strategies Data organisation, modelling, and access to support reporting, analytics, and operational decision‑making Scalability, cost efficiency, performance, security, and long‑term maintainability Standards for naming, schema organisation, access control, environment separation, and change management Data Modeling & Semantic Layer Design Design and maintain core data models representing 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 semantic layer standards so business definitions, calculations, KPIs, and measures are applied consistently across tools and teams. 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. 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. Act as 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 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; business metric definitions; transformation logic and business rules; data lineage and source‑system dependencies; standards, guardrails, and best practices. 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 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 optimisation Strong experience with dimensional and analytical data modelling 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, explaining data structures, standards, and technical decisions to both technical and non‑technical audiences Preferred Qualifications Deep experience with Snowflake performance optimisation, 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. Mustang Cat is an Equal Opportunity Employer. #J-18808-Ljbffr Mustang Cat Inc.