Senior Data Engineer

Title: Sr. Data EngineerLocation: Sugarland TX - 4 days/week onsite - Locals OnlyDuration: 12 monthsDesign, develop, and support data engineering, data modeling, and data integrations, with a primary focus on accelerating data landing and curation in a Databricks data lake house. Build and maintain reliable, well-governed pipelines that ingest data from source systems into the lake house and curate it through a layered (medallion) architecture into analytics-ready, trusted datasets. The role also carries a strong reporting and data-analysis focus — partnering with business users to build semantic data models, dashboards, and reports, and performing hands-on analysis to answer business questions. The Data Engineer will help establish the data foundation that powers data-related AI and machine learning initiatives, ensuring high-quality, well-documented, AI-ready data products.Key ResponsibilitiesBuild, optimize, and support pipelines that land data from source systems into the Databricks lake house and curate it through a layered (medallion) architecture into trusted, analytics-ready datasets.Produce and maintain high-quality, well-governed, documented, AI-ready data products that serve as the foundation for AI and machine learning initiatives.Implement data quality, governance, and monitoring controls (e.g., Unity Catalog, automated testing, alerting) across lake house pipelines.Develop and maintain reporting and analytics solutions — semantic data models, dashboards, and reports — and perform ad-hoc querying to support business decision-making.Gather requirements, design, and develop new data integrations or enhancements to existing code.Partner with business users and the Business Relationship Management team on requirements gathering, testing, and supporting existing integrations, analytics, and reporting.Create and maintain documentation and process flows for integration solutions.Required Experience & SkillsMinimum 5 years of IT/technology experience spanning data analysis, data engineering, and/or data integration, with a focus on building and curating pipelines in a cloud data lake or lake house environment.At least 3 years writing SQL/NoSQL queries, with specific experience in MS SQL Server, Oracle, and/or Postgres.Hands-on experience with a modern cloud data platform / lake house (Databricks, Microsoft Fabric, Snowflake, or comparable). Databricks strongly preferred.Demonstrated experience landing data from diverse source systems into a lake/lake house and curating it through a medallion (bronze-silver-gold) architecture into clean, conformed, analytics-ready datasets.Strong Python skills for data engineering, including PySpark.Working knowledge of data quality, data governance, and pipeline reliability practices — automated testing, monitoring, alerting, and orchestration of batch and incremental/streaming workloads.Experience designing simplified data models for integrations, analytics, and reporting; comfortable performing hands-on data analysis and ad-hoc querying.Experience extracting data from source systems via web services (SOAP, REST, Web APIs), XML, and CSV/Excel exports.Experience building the data foundation and automation pipelines for analytics and AI/ML initiatives, and partnering with business users on LLM/GenAI use cases.Bachelor's degree in Information Systems, IT, or a related technical discipline — or equivalent demonstrated technical proficiency.Strong interpersonal and communication skills; fluent in English (oral and written).Preferred / Nice-to-HavePython, cloud data warehouse experience (e.g., Snowflake, Synapse), Spark SQLPerformance tuning, partitioning, and optimization.Modern LLM architectures and GenAI frameworks — retrieval-augmented generation (RAG), embeddings and vector databases, prompt orchestration, and integrating LLMs into data products and pipelines.Familiarity with using LLMs in automation development and with vector/embedding data.Experience in the Oil & Gas domain.