Data Analytics Engineer

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.

Databricks Data Engineering

• 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.

ComboCurve Integration & Enablement

• 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.

Spotfire Analytics Enablement

• 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.

Data Quality & Governance

• 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. *, * 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. *

• 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.

Business & Leadership Skills

• 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.

Preferred Qualifications

• 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.