Data Analytics Engineer

Key Responsibilities

Data Product Ownership

• Design, develop, and maintain analytics-ready data products supporting forecasting, economics, planning, drilling, completions, and production workflows.
• Establish trusted datasets and business logic that serve as authoritative sources for key analytical processes.
• Partner with business stakeholders to prioritize data product enhancements and new capabilities.

• Develop scalable data pipelines and transformation processes within Databricks using SQL and Python.
• Integrate data from operational, corporate, and third-party sources into a unified analytical environment.
• Optimize data models and architectures to improve performance, usability, and scalability.
• Implement automated workflows that reduce manual data preparation and spreadsheet-based processes.

• Support and enhance data flows feeding ComboCurve forecasting and economic workflows.
• Ensure forecasting and economic analyses are based on consistent, validated, and auditable datasets.
• Collaborate with engineers and economists to streamline data preparation and model execution.
• Improve traceability and transparency of inputs used in forecasting and economic evaluations.

• Develop and maintain curated datasets optimized for Spotfire dashboards and self-service analytics.
• Partner with stakeholders to improve data accessibility and visualization capabilities.
• Ensure consistency between Spotfire reporting, ComboCurve outputs, and enterprise data sources.
• Enable users to gain insights quickly through well-designed analytical data structures.

• Establish data quality standards, validation rules, and monitoring processes.
• Identify and resolve data inconsistencies across systems and workflows.
• Develop business metadata, documentation, lineage, and governance practices.
• Promote a culture of data accountability and continuous improvement.

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Required Qualifications

Technical Expertise

• Bachelor's degree in Engineering, Computer Science, Data Science, Information Systems, or related discipline.
• 5+ years of experience in analytics engineering, data engineering, business intelligence, or advanced analytics.
• Advanced SQL skills and proven experience working with large, complex datasets.
• Strong Python programming experience for data engineering and analytics applications.
• Hands-on experience developing solutions within Databricks.
• Experience designing and maintaining modern ELT/ETL pipelines.
• Understanding of data modeling, data architecture, and data product design principles.

Analytics Platforms

• Experience developing analytics solutions using Spotfire.
• Experience supporting or integrating analytical applications and business workflows.
• Strong understanding of how data structures influence reporting, forecasting, and decision-making.

• Strong problem-solving and analytical thinking capabilities.
• Ability to translate business challenges into scalable technical solutions.
• Excellent communication and stakeholder management skills.
• Demonstrated ability to influence across technical and business teams.

• Experience in Oil & Gas, particularly Unconventional Resources.
• Experience with ComboCurve forecasting and economic evaluation workflows.
• Understanding of:
• Reservoir Engineering
• Asset Planning
• Economics & Capital Allocation
• Familiarity with data governance and master data management practices.
• Experience developing enterprise-scale analytical data products.

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• Within the first year, the successful candidate will:
• Establish trusted and governed datasets supporting major forecasting and economics workflows.
• Reduce time engineers and analysts spend validating and preparing data.
• Improve consistency across Databricks, ComboCurve, Spotfire, and enterprise reporting systems.
• Implement automated data quality monitoring and exception reporting.
• Create scalable data foundations that accelerate future analytics and AI initiatives.

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Hiring Manager Note

• The mission of this role is simple: Make trusted data easy to find, easy to understand, and easy to use. Success will be measured not by the number of dashboards or models built, but by the confidence our engineers and business teams have in the data that powers their decisions.