Cactus Wellhead - Data Engineer & Architect

Cactus Wellhead•Piney Point Village, TX

2d•Hybrid

About The Position

The Senior Data Engineer / Data Architect is responsible for designing, building, and governing the enterprise data platform on Azure Databricks. This role owns both hands-on data engineering delivery and data architecture design, ensuring that enterprise data is reliable, scalable, secure, and ready to support analytics, reporting, and AI initiatives. The position plays a critical role in transforming data from ERP, CRM, and other enterprise systems into trusted, reusable data products that enable business insights and future AI capabilities. The role operates with high ownership across the full lifecycle, from ingestion and transformation to data modeling, governance, and platform optimization, aligned with a modern Lakehouse architecture.

Requirements

6–10+ years of experience in data engineering, data architecture, or enterprise data platforms
Hands-on experience building and supporting data pipelines in cloud environments
Experience working with enterprise systems (ERP, CRM, or similar)
Strong expertise in Azure Databricks / Apache Spark
Strong expertise in Python / PySpark
Strong expertise in SQL (advanced)
Experience with Azure Data Lake Storage (ADLS)
Experience with CI/CD with Github
Experience with API and data integration patterns
Strong understanding of data modeling (dimensional and normalized)
Strong understanding of data governance and security (RBAC, lineage)

Nice To Haves

Experience with Delta Lake and Lakehouse architecture
Exposure to ML/AI pipelines or data science workflows
Experience in manufacturing, oil & gas, or ERP-heavy environments
Familiarity with modern programming languages and frameworks (C#, .NET, SQL, JavaScript)
Experience with DevOps, CI/CD, and source control for ERP development
Knowledge of cloud-based ERP solutions and migration strategies
Strong background in data integration, ETL, and reporting tools (Power BI, SSRS, Tableau)

Responsibilities

Design and build end-to-end data pipelines (batch and near real-time) using Databricks and Spark
Implement and maintain Bronze / Silver / Gold data architecture layers
Ingest data from ERP, SaaS applications, APIs, and legacy systems into Lakehouse
Optimize pipelines for performance, scalability, and cost efficiency
Implement CI/CD pipelines using GitHub
Define and maintain the enterprise data architecture and standards
Design logical and physical data models across core business domains (finance, operations, field service)
Establish consistent definitions for critical entities (customer, job, revenue, etc.)
Ensure data structures support both analytics and AI/ML workloads
Implement and manage Unity Catalog–based governance (RBAC, lineage, auditability)
Define and enforce data standards for naming, access control, and data quality
Ensure compliance with security policies (e.g., sensitive HR and financial data protection)
Establish monitoring, validation, and reconciliation processes to ensure data accuracy and reliability
Define scalable patterns for data ingestion, transformation pipelines, and data access (BI tools, APIs, AI models)
Integrate Databricks with Azure services (ADLS, APIs, enterprise systems)
Ensure platform reliability, observability, and operational excellence
Prepare and structure data for AI/ML use cases and automation initiatives
Partner with analytics and AI teams to deliver datasets supporting predictive models and AI-driven workflows
Enable reusable datasets and feature-ready data pipelines
Partner with business stakeholders, application teams, and analytics teams to translate requirements into scalable solutions
Provide technical leadership for data engineering best practices and architecture decisions
Mentor junior engineers or contractors as the platform scales