Data Product Engineer (Databricks)

VDart Inc; Dallas (TX); Hybrid; USD 140,000 - 190,000; Part time

Job summary

VDart Inc. seeks a Data Product Engineer (Databricks) in Dallas, TX (Hybrid) for a contract engagement.

Lead architecture and implementation of enterprise Data Product standards within the Databricks ecosystem, driving reusable frameworks and governance across data products. You will partner with business, risk, and technology leadership to identify high-value data products, build MVPs, and guide delivery with CI/CD, monitoring, and security controls.

Qualifications

• Minimum 10+ years of experience in Data Engineering, Data Architecture, Analytics Engineering, or Enterprise Data Management.
• Minimum 5+ years designing and implementing cloud-based data platforms and data products.
• Minimum 4+ years hands-on Databricks experience (Delta Lake, Unity Catalog, Workflows, MLflow, Lakehouse patterns).
• Minimum 5+ years designing scalable data architectures and distributed data processing solutions.
• Minimum 4+ years building enterprise data pipelines using Spark, Python, SQL, and cloud-native tech.
• Minimum 3+ years delivering cloud-based solutions on Azure, AWS, or GCP.
• Experience in data governance, metadata management, data quality, lineage, and security in modern platforms.
• Experience creating reusable engineering standards and platform accelerators for large-scale adoption.
• Excellent ability to translate business requirements into scalable data product solutions.
• Strong communication and consulting skills for architecture workshops and executive reviews.

Responsibilities

• Lead design and implementation of enterprise Data Product standards, patterns, and engineering practices in Databricks.
• Partner with stakeholders to identify high-value data products supporting critical analytics outcomes.
• Define reusable data product frameworks, governance controls, metadata standards, and quality requirements.
• Architect and build end-to-end MVP Data Products in Databricks including ingestion, transformation, quality, metadata, security, lineage, and consumption layers.
• Develop scalable data pipelines and Lakehouse solutions using Databricks, Delta Lake, Unity Catalog, Spark, and cloud services.
• Collaborate with Data Engineers, Data Architects, Governance, and platform teams to establish delivery standards for product-based data management.
• Design and implement data product templates, CI/CD patterns, monitoring, observability, and automated testing frameworks.
• Evaluate existing data assets and capabilities to identify modernization opportunities and adoption improvements.
• Create proof-of-value demos and MVPs to validate architecture decisions and demonstrate business value.
• Lead architecture workshops and stakeholder discussions on data contracts, interoperability, discoverability, and consumption patterns.

Skills

Data Engineering

Data Architecture

Analytics Engineering

Enterprise Data Management

Databricks

Delta Lake

Unity Catalog

Spark

Python

SQL

Tools

Databricks

Delta Lake

Unity Catalog

Spark

MLflow

Workflows

Job description

Title: Data Product Engineer (Databricks)

Locations: Dallas,TX (Hybrid)

Type: Contract

Day to Day Job Duties:

• Lead the design and implementation of enterprise Data Product standards, architecture patterns, and engineering practices within the Databricks ecosystem.
• Partner with business stakeholders, Risk domain teams, Data Product Owners, and technology leadership to identify high-value data products that support critical business use cases and analytics outcomes.
• Define and implement reusable data product frameworks, reference architectures, governance controls, metadata standards, and quality requirements to accelerate enterprise adoption.
• Architect and build multiple end-to-end MVP Data Products in Databricks, including data ingestion, transformation, quality controls, metadata management, security, lineage, and consumption layers.
• Develop scalable data pipelines and Lakehouse solutions utilizing Databricks, Delta Lake, Unity Catalog, Spark, and cloud-native services.
• Collaborate with Data Engineers, Data Architects, Governance teams, and platform teams to establish delivery standards that support product-based data management and domain-oriented ownership.
• Design and implement data product templates, CI/CD patterns, operational monitoring, observability controls, and automated testing frameworks to support repeatable delivery.
• Evaluate existing data assets, business processes, and technology capabilities to identify opportunities for modernization and improved data product adoption.
• Create proof-of-value demonstrations and working MVP solutions that validate architecture decisions, demonstrate business value, and establish implementation patterns for future teams.
• Lead technical workshops, architecture reviews, and stakeholder discussions focused on data product design, data contracts, interoperability, discoverability, and consumption patterns.
• Provide technical leadership and mentorship to engineering teams while driving alignment between strategic business objectives and practical delivery execution.
• Support proposal development, solution architecture discussions, effort estimation, and client presentations related to enterprise Data Product and Databricks transformation initiatives.

Basic Qualifications:

• Minimum 10+ years of experience in Data Engineering, Data Architecture, Analytics Engineering, or Enterprise Data Management.
• Minimum 5+ years of experience designing and implementing modern cloud-based data platforms and data products.
• Minimum 4+ years of hands‑on Databricks experience, including Delta Lake, Unity Catalog, Workflows, MLflow, and Lakehouse architecture patterns.
• Minimum 5+ years of experience designing scalable data architectures and distributed data processing solutions.
• Minimum 4+ years of experience building enterprise data pipelines using Spark, Python, SQL, and cloud-native technologies.
• Minimum 3+ years of experience implementing Data Product operating models, Data Mesh concepts, domain‑driven data ownership, or product‑oriented data delivery approaches.
• Minimum 3+ years of experience delivering cloud-based solutions on Azure, AWS, or Google Cloud Platform.
• Experience implementing data governance, metadata management, data quality frameworks, lineage, and security controls within modern data platforms.
• Experience designing reusable engineering standards, architecture patterns, and platform accelerators that support large‑scale enterprise adoption.
• Experience engaging directly with business stakeholders to translate business requirements into scalable data product solutions.
• Strong communication and consulting skills with the ability to lead architecture workshops, executive discussions, and technical solution reviews.