Senior Data Engineer, Integrations & AI

PakEnergy•Oklahoma City, OK

2d•Hybrid

About The Position

The Senior Data Engineer, Integrations & AI designs and develops scalable data solutions supporting business intelligence, analytics, artificial intelligence, and operational decision-making across a growing SaaS organization. The role integrates data across systems, builds reliable data models and pipelines, improves data quality, and creates trusted datasets for reporting, automation, and AI-driven solutions. The position is hybrid and based in Oklahoma City, Oklahoma. The role leads land-data conversions and client integrations, develops and maintains data pipelines and integrations, establishes data-quality and governance practices, and supports AI and advanced analytics. It partners with technical and business teams and serves as a technical contact for assigned client integrations following go-live. Success includes more reliable and accessible data, increased process automation, consistent metrics, faster access to trusted data, and scalable solutions for reporting, analytics, and AI.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Data Analytics, Engineering, Mathematics, or a related field, or equivalent professional experience.
7+ years of experience in data engineering, data integrations, implementations, business intelligence, data analytics, or a related technical discipline.
Advanced proficiency in SQL, including experience working with complex relational datasets.
Experience developing and maintaining ETL/ELT pipelines, data integrations, and data transformations.
Experience integrating data across multiple systems and data sources.
Experience working with oil and gas land data, including leases, tracts, agreements, wells, owners, and divisions of interest, or demonstrated ability to quickly learn complex land data structures and business processes.
Strong analytical and problem-solving skills, with the ability to translate business requirements into scalable technical solutions.
Strong written and verbal communication skills and the ability to collaborate effectively across technical and business teams.
Employment eligibility to work in the United States; the company will not pursue visa sponsorship for this position. Candidates are responsible for maintaining and renewing any visas or permits that grant employment eligibility.

Nice To Haves

Experience with PakLand, Quorum, P2, Enertia, Integra, Bolo, Enverus, or comparable oil and gas applications and data sources.
Experience with Python or another data-oriented programming language.
Experience with cloud-based data platforms, integration services, and APIs, including structured and semi-structured data.
Experience with Microsoft Power BI or comparable business intelligence and visualization platforms.
Experience with PowerShell, secure file-transfer processes, or automation of recurring inbound and outbound data exchanges.
Familiarity with generative AI, large language models, AI assistants, or AI-assisted development and analytics.
Experience preparing or engineering data for AI/ML, predictive analytics, or other advanced analytical applications.
Experience with statistical analysis, forecasting, segmentation, anomaly detection, or predictive modeling.
Experience integrating CRM, ERP, customer support, product, financial, or professional services data.
Previous experience within a B2B SaaS or enterprise software organization.

Responsibilities

Lead end-to-end conversion of land data from legacy and competitor systems into PakLand, including source analysis, field mapping, transformation, validation, reconciliation, and client acceptance.
Build, operate, monitor, and support recurring integrations that load client accounting and operational data, including wells, owners, divisions of interest, and revenue history, into PakLand.
Design and maintain processes for associating converted documents with the appropriate agreements and records using metadata, indexing, or established document-folder structures.
Develop and maintain integrations with external well and industry-data providers, applying client-specific business rules to identify and resolve conflicts among source systems.
Design, develop, and maintain scalable data pipelines and integrations across business and product systems.
Build and optimize complex SQL queries, transformations, stored procedures, and reusable datasets.
Develop and maintain ETL/ELT processes that integrate information from multiple data sources.
Work with relational databases, APIs, flat files, and structured and semi-structured data, including CSV and JSON.
Develop scalable data models supporting analytics, reporting, operational processes, and AI applications.
Troubleshoot data integration, transformation, performance, and reliability issues.
Automate manual data preparation, reconciliation, and recurring data processes where appropriate.
Establish validation and reconciliation processes to improve data accuracy, consistency, and reliability.
Identify data-quality issues and partner with technical and business teams to address root causes.
Document data definitions, business rules, transformations, lineage, and integration requirements.
Help establish trusted definitions and consistent methodologies for key business metrics.
Support appropriate data access, security, privacy, and governance practices.
Develop and maintain reliable data foundations for AI, advanced analytics, automation, and predictive applications.
Partner with technical and business teams to identify and evaluate practical AI use cases.
Use AI-assisted development and analytical tools to improve productivity and accelerate insight generation.
Support datasets and workflows used for forecasting, segmentation, anomaly detection, predictive analytics, and other advanced analytical use cases.
Validate AI-supported outputs against trusted source data and established business rules.
Support responsible use of proprietary and customer information within AI-enabled solutions.
Stay current on developments in generative AI, modern data platforms, and analytical technologies relevant to a SaaS company.
Collaborate across Product, Engineering, Finance, Operations, Sales, Customer Success, Support, and Professional Services.
Lead client data-conversion and integration kickoff meetings.
Assess source data and help define scope, assumptions, dependencies, estimates, and deliverables for statements of work.
Communicate conversion status, validation results, data-quality concerns, and implementation risks to client and internal stakeholders.
Serve as a primary technical contact for assigned client integrations following go-live.
Translate business problems into clearly defined data requirements and technical solutions.
Communicate complex data concepts and findings to technical and non-technical audiences.
Balance immediate business needs with long-term scalability, maintainability, and data quality.