Part Time Databricks Jobs (NOW HIRING)

Since 1898, we have helped customers complete more than 25,000 projects in 160 countries on all seven continents that have created jobs, grown economies, improved the resiliency of the world's infrastructure, increased access to energy, resources, and vital services, and made the world a safer, cleaner place. 

Differentiated by the quality of our people and our relentless drive to deliver the most successful outcomes, we align our capabilities to our customers' objectives to create a lasting positive impact. We serve the Infrastructure; Nuclear, Security & Environmental; Energy; Mining & Metals, and the Manufacturing and Technology markets. Our services span from initial planning and investment, through start-up and operations. 

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The Execution Transformation initiative is delivering AI and analytics capabilities at project scale. Effective data readiness is the foundation of successful AI deployment: without reliable, structured, and accessible data, AI and analytics solutions cannot deliver value across the project portfolio. Preparing work products and data elements so they are accurate, complete, and fit for project use & AI consumption is foundational to successful data-driven execution. 

The AI Data Readiness Lead is a technical role responsible for assessing, preparing, and maintaining the data that underpins project data. Initially the role will focus on Combined Cycle Gas Turbine (CCGT) and the evolve to support other Infrastructure business lines and sectors. Working within the Unified Data Platform (Databricks), the role ingests, cleanses, structures, and quality-assures CCGT project data and keeps the Clarity data-readiness environment complete and current. This role reports to the AI Program Manager and works in close collaboration with the Data Solutions Architect on the technical approach to data readiness & the Dashboard & Analytics Lead on data quality reporting. 

RM1

•    Working with the Functional AI Leads to assess data readiness across CCGT projects, conducting structured data assessments and gap analysis of the engineering work products and data elements held by each function to determine what is required to make that data fit for project execution, AI, analytics, and machine-learning use. 
•    Collect and structure the work products and data elements by function into Clarity, the data-readiness component of the Unified Data Platform (Databricks), so that CCGT project data is captured consistently and at scale. 
•    Build and maintain the data ingestion routines and pipelines that load CCGT project data into the Unified Data Platform, ensuring data flows reliably from source systems into the readiness environment. 
•    Cleanse, standardise, and structure raw project data by applying consistent schemas, naming, and formats, so that data is accurate, complete, and ready for downstream AI, analytics, and reporting. 
•    Keep Clarity fully updated and maintained with all work products and data elements for active CCGT projects, coordinating with the Data Solutions Architect on the technical approach to data readiness. 
•    Define, measure, and monitor data quality and completeness across the accuracy, completeness, timeliness, and consistency dimensions, profiling incoming data and remediating issues at source. 
•    Coordinate with the Dashboard & Analytics Lead (CCGT) to develop data quality and completeness reports and dashboards that give the programme clear visibility of data readiness. 
•    Coordinate with the Functional AI Leads to source, validate, and prepare the datasets required for prioritised CCGT AI and analytics use cases. 
•    Maintain metadata, data lineage, and technical documentation for CCGT datasets to support traceability, reproducibility, and governance across the AI lifecycle. 
•    Apply data governance, access, and data-handling standards to CCGT data, ensuring that sensitive or controlled information is managed appropriately within the Unified Data Platform. 
•    Identify, track, and escalate data gaps, risks, and dependencies that could affect the readiness of data for CCGT AI use cases, proposing and implementing practical remediation 

• Bachelor's degree in Engineering, Project Management, Computer Science, Business Administration, or a related field.
• 10 years or more of experience in programme or project delivery, with at least 3 years in a technology, data, or digital transformation role.

Bachelor's degree in Computer Science, Data Engineering, Information Systems, Engineering, or a related field. 

• 5 years or more of experience in data engineering, data management, or data quality roles, ideally supporting analytics or AI/ML workloads. 

• Proficiency in SQL and Python for data ingestion, transformation, cleansing, and validation. 

• Hands-on experience building and maintaining data pipelines (ETL/ELT) and preparing data for downstream analytics or machine learning. 

• Practical experience with Databricks, or an equivalent lakehouse or Spark-based platform, including Delta Lake and notebook-based data processing. 

• Strong understanding of data quality dimensions (accuracy, completeness, timeliness, and consistency) with experience profiling, validating, and remediating data. 

• Working knowledge of data modelling, schema design, and data standardisation across heterogeneous source systems. 

• Familiarity with data governance, metadata management, and data lineage practices, including the handling of sensitive or controlled data. 

• Ability to communicate data-readiness status, gaps, and requirements clearly to both technical and non-technical stakeholders. 

Preferred Qualifications 

• Experience preparing engineering, construction, or project data in an EPC, infrastructure, or capital-projects environment. 

• Experience with Power BI or similar tools to build data quality and completeness dashboards. 

• Understanding how data readiness supports machine learning and generative AI, including feature preparation and retrieval-augmented generation (RAG) data sources. 

• Experience with version control (Git) and DataOps or CI/CD practices for data pipelines. 

• Knowledge of Bechtel project delivery processes, engineering data standards, and CCGT programme objectives. 

• Relevant certification in data engineering, a cloud data platform, or Databricks. 

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