Kelly

Senior Analytics Engineer – Databricks / SQL / Python

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Analytics Engineer with a 1-year contract, based fully onsite in Spring, TX. Key skills include advanced SQL, strong Python experience, and familiarity with Databricks. Oil & Gas industry experience, particularly in Unconventional Resources, is preferred.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
August 19, 2026
🕒 - Duration
More than 6 months
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🏝️ - Location
On-site
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Spring, TX
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🧠 - Skills detailed
#Data Architecture #Data Governance #Data Engineering #Programming #Data Modeling #Data Management #Datasets #Data Access #Python #Databricks #Documentation #BI (Business Intelligence) #"ETL (Extract #Transform #Load)" #Forecasting #Data Quality #MDM (Master Data Management) #Visualization #SQL (Structured Query Language) #AI (Artificial Intelligence) #Spotfire #Scala #Leadership #Data Documentation #Data Science #Computer Science #Data Pipeline #Monitoring #Metadata
Role description
Duration: 1 year Location: Spring TX (FULLY ONSITE) Top 3 skill sets/technologies required for qualification: • 1): Proven Advanced SQL skills and proven experience working with large, complex datasets. • 2): Strong Python programming experience for data engineering and analytics applications. • 3): Strong problem-solving and analytical thinking capabilities. About the Role • We are seeking a Senior Unconventional Analytics Engineer to ensure data-driven decision making are intuitive and reliable across our Unconventional organization. • Over the past two years, our team has developed forecasting, economic, and operational analytics solutions leveraging Databricks, ComboCurve, and Spotfire. While these tools provide significant value, their effectiveness depends on consistent, trusted, and easily accessible data. • This role will focus on building and managing the data products that power our forecasting, economics, planning, and operational workflows. The successful candidate will work closely with reservoir engineers, production engineers, planners, economists, and data professionals to ensure critical business data is reliable, governed, automated, and readily available for analytics and decision support. 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. • 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. 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. 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. What Success Looks Like • 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.