

Business Data Analyst - Oil and Gas
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Business Data Analyst in Oil and Gas, requiring 5+ years of experience in data analysis, SQL, and Python. Contract length and pay rate are unspecified. Strong knowledge of trading environments and Agile practices is essential.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
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🗓️ - Date discovered
September 25, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Unknown
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
London Area, United Kingdom
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🧠 - Skills detailed
#Migration #Business Analysis #Metadata #Data Reconciliation #Data Catalog #Data Engineering #Data Governance #Data Pipeline #Azure DevOps #Data Migration #Databricks #DevOps #Visualization #Data Analysis #"ETL (Extract #Transform #Load)" #Data Management #Azure #UAT (User Acceptance Testing) #Agile #Microsoft Power BI #Snowflake #BI (Business Intelligence) #Data Quality #Regression #Data Profiling #Cloud #Datasets #Databases #Tableau #SQL (Structured Query Language) #Python #Jira
Role description
Note : Must Have (Oil and Gas)
Technical Data Business Analyst - Digital Trading AnalyticsMinimum years of experience: 5+ yearsRole OverviewWe are looking for a hands-on Technical Data Business Analyst to support the delivery of data-centric initiatives within the Digital Trading Analytics (dTA) portfolio. This role is ideal for someone with a strong foundation in data analysis, data management, data quality, and stakeholder engagement—particularly in the context of trading, market data, and analytics platforms.You will work closely with data engineers, platform teams, and business users to define, test, and validate data solutions that support trading and analytics use cases. The role requires a strong understanding of data migration, reconciliation, and testing practices, as well as the ability to translate business needs into clear, testable data requirements.Key Responsibilities
• Define and document business and data requirements for analytics and reporting solutions.
• Perform data profiling, gap analysis, and root cause investigations to ensure data quality and consistency.
• Lead and support data migration projects, including mapping, validation, and reconciliation of large datasets across platforms.
• Collaborate with engineering teams to validate data pipelines and transformations.
• Design and execute test plans for data products, including functional, regression, and UAT testing.
• Work with market data feeds and reference data to support trading analytics use cases.
• Translate complex business needs into structured data models and reporting logic.
• Engage with stakeholders across trading, analytics, and technology to gather requirements and provide updates.
• Support Agile delivery by contributing to backlog grooming, sprint planning, and story refinement.Required Skills & Experience
• Strong experience in data analysis, data reconciliation, and data testing.
Hands-on experience with SQL and Python.
• Experience in energy (oil & gas) and financial trading environments.
• Familiarity with relational databases and data warehousing concepts.
• Experience working with market data, trading data, or financial reference data.
• Understanding of Agile delivery practices and tools (e.g. Azure DevOps, JIRA).
• Excellent communication skills and ability to work with both technical and non-technical stakeholders.Nice to Have
• Exposure to data visualization tools (e.g. Power BI, Tableau).
• Familiarity with cloud-based data platforms (e.g. Databricks, Snowflake)
.
• Knowledge of data governance, lineage, and metadata management
.
• Exposure to data cataloguing tools and data quality frameworks. Release Comments:
Note : Must Have (Oil and Gas)
Technical Data Business Analyst - Digital Trading AnalyticsMinimum years of experience: 5+ yearsRole OverviewWe are looking for a hands-on Technical Data Business Analyst to support the delivery of data-centric initiatives within the Digital Trading Analytics (dTA) portfolio. This role is ideal for someone with a strong foundation in data analysis, data management, data quality, and stakeholder engagement—particularly in the context of trading, market data, and analytics platforms.You will work closely with data engineers, platform teams, and business users to define, test, and validate data solutions that support trading and analytics use cases. The role requires a strong understanding of data migration, reconciliation, and testing practices, as well as the ability to translate business needs into clear, testable data requirements.Key Responsibilities
• Define and document business and data requirements for analytics and reporting solutions.
• Perform data profiling, gap analysis, and root cause investigations to ensure data quality and consistency.
• Lead and support data migration projects, including mapping, validation, and reconciliation of large datasets across platforms.
• Collaborate with engineering teams to validate data pipelines and transformations.
• Design and execute test plans for data products, including functional, regression, and UAT testing.
• Work with market data feeds and reference data to support trading analytics use cases.
• Translate complex business needs into structured data models and reporting logic.
• Engage with stakeholders across trading, analytics, and technology to gather requirements and provide updates.
• Support Agile delivery by contributing to backlog grooming, sprint planning, and story refinement.Required Skills & Experience
• Strong experience in data analysis, data reconciliation, and data testing.
Hands-on experience with SQL and Python.
• Experience in energy (oil & gas) and financial trading environments.
• Familiarity with relational databases and data warehousing concepts.
• Experience working with market data, trading data, or financial reference data.
• Understanding of Agile delivery practices and tools (e.g. Azure DevOps, JIRA).
• Excellent communication skills and ability to work with both technical and non-technical stakeholders.Nice to Have
• Exposure to data visualization tools (e.g. Power BI, Tableau).
• Familiarity with cloud-based data platforms (e.g. Databricks, Snowflake)
.
• Knowledge of data governance, lineage, and metadata management
.
• Exposure to data cataloguing tools and data quality frameworks. Release Comments: