Engineering Supervisor Data and Applications

Beusa Energy Group•The Woodlands, TX

4d•Onsite

About The Position

The Data Engineering Supervisor is a hands-on player-coach responsible for leading a team of data engineers to deliver high-quality, maintainable data solutions on our Azure Databricks-centered data platform. This role is accountable for day-to-day team execution, sprint planning, workload management, and delivery quality across ingestion, transformation, and data product development. The Data Engineering Supervisor does not own platform architecture or technical standards. Instead, they execute within the patterns and guardrails defined by the Principal Architect–Data Platforms and within the governance and delivery framework led by the Data Governance & Delivery Manager. Success in this role means turning architectural designs and delivery plans into robust, repeatable implementations, developing the team’s skills, and ensuring predictable delivery against business and analytics needs.

Requirements

Successfully passes all applicable general pre-employment testing including but not limited to: background check, pre-employment drug screening, pre-employment fit tests, pre-employment aptitude and/or competency assessment(s).
Proficiency in the spoken English language.
Position requires daily in-person, predictable attendance.
Valid U.S. Driver’s License required.
Employment is contingent upon meeting company driving standards, including an acceptable Motor Vehicle Record (MVR) in accordance with Company policy.
Bachelor’s degree in Engineering, Information Systems, Computer Science, or a related field, or equivalent practical experience
5+ years of professional experience in data engineering or software engineering with a strong data focus.
3+ years of experience managing data engineering or BI teams.
1+ years of experience in a team lead, senior engineer, or supervisor role (formal or informal), with demonstrated coaching and delegation.
Hands-on experience with: Cloud-based data platforms (preferably Azure).
Databricks, Spark, SQL, and data pipeline orchestration.
Building and maintaining production data pipelines and data models.
Demonstrated ability to plan and manage team workloads, align delivery to priorities, and communicate status and risks clearly.

Nice To Haves

Experience working on an Azure Databricks-centered modern data platform.
Familiarity with data modeling techniques (e.g., dimensional modeling, data vault) and BI/analytics consumption patterns.
Experience working in a matrixed environment with product, BI/analytics, and architecture stakeholders.
Proven ability to improve engineering practices (code review, CI/CD, testing, observability).
Strong understanding of modern data engineering concepts, including ELT/ETL, data modeling, and data pipeline operations.
Experience implementing solutions within an established data platform architecture and standards framework.
Knowledge of data quality, metadata, and lineage concepts and how they are operationalized in pipelines and models.
Ability to collaborate with technical architects and engineering leaders, recognizing that final technical design and platform decisions are owned by the Principal Architect–Data Platforms and Principal Architect–Applications Engineering, while delivery planning and governance are led by the Data Governance & Delivery Manager.
Excellent verbal and written communication skills, with the ability to articulate complex data concepts to both technical and non-technical audiences, including executives.
Strong stakeholder management skills, with the ability to build trust and maintain alignment across BI/Analytics, AI/ML, IT, and business partners.
Proven ability to collaborate effectively with cross-functional teams (Architecture, AI/ML, IT, Security, Business).
Ability to translate business needs into clear, prioritized delivery plans for the Data Engineering team in partnership with the Data Governance & Delivery Manager.
Strong analytical and problem-solving abilities, particularly in identifying and resolving data-related issues and delivery bottlenecks.
Ability to make data-driven decisions and recommendations.
A high degree of curiosity and a desire to stay current with industry trends in modern data engineering, data platforms, and delivery methodologies.
Openness to feedback and a continuous improvement mindset for processes and team performance.
Oil & Gas industry experience is a plus.

Responsibilities

Team Leadership & Delivery Management: Lead, coach, and develop a team of data engineers responsible for ingestion, transformation, and data product development.
Partner closely with the Data Governance & Delivery Manager on sprint planning, backlog grooming, and aligning the team’s work to overall delivery priorities and timelines.
Manage task assignment and day-to-day execution for the Data Engineering team, escalating delivery risks and capacity constraints to the Data Governance & Delivery Manager promptly.
Ensure work is estimated, prioritized, and delivered on time and with high quality, in alignment with business priorities and data product roadmaps.
Provide regular status updates and delivery risk visibility to the Data Governance & Delivery Manager and relevant stakeholders.
Execution of Data Platform Designs: Translate solution designs, patterns, and technical standards from the Principal Architect – Data Platforms into executable work items and implementation plans.
Ensure the team adheres to approved architectures, coding standards, data modeling patterns, and security/compliance requirements.
Enforce best practices in code quality, testing, deployment, observability, and documentation for all data engineering work.
Data Products & Analytics Support: Partner with BI/Analytics, AI/ML, and business stakeholders to understand data requirements and translate them into engineering tasks, coordinating prioritization with the Data Governance & Delivery Manager.
Ensure data pipelines and models are built to reliably support downstream consumption (dashboards, self-service analytics, AI/ML workloads).
Support incident response and root-cause analysis for data quality, performance, or reliability issues affecting data products, coordinating communication and remediation plans with the Data Governance & Delivery Manager.
People Management & Growth: Conduct regular 1:1s, performance feedback, and career development conversations with data engineering team members.
Identify skills gaps and training needs; champion continuous improvement in Databricks, Spark, SQL, and modern data engineering practices.
Foster a culture of ownership, accountability, and collaboration within the team.
Governance, Compliance, and Standards Enforcement: Ensure the team’s work complies with data governance policies, access controls, and security/privacy requirements.
Monitor adherence to data quality standards and operational SLAs, escalating risks or systemic issues to Data Governance & Delivery Manager and Principal Data Architect as needed.
Provide practical feedback to the Data Governance & Delivery Manager and the Principal Data Architect on where standards or patterns need refinement based on real-world usage.
Continuous Improvement: Continuously seek opportunities to improve data engineering practices, team workflows, delivery methodologies, and stakeholder communication, and provide feedback to the Data Governance & Delivery Manager on how governance processes are working in practice.
Stay informed on industry best practices in data engineering
Perform other related duties as assigned to assist with successful operations and business continuity.