Manoj Maharana

• Designed and implemented automated data mapping pipelines for RBU frac projects using Python, reducing manual effort and accelerating reporting turnaround.
• Built an AI-driven reporting environment (ARD data profile) for wells and rigs, delivering cost optimization through end-to-end automation and advanced analytics.
• Established data quality and compliance standards, achieving 99.95% uptime and ensuring reliability of real-time drilling data delivery.
• Developed MCDEBIE use case, integrating email-to-SharePoint-to-ADLS workflows with Synapse and ADF for cross-platform integration and streamlined data management.
• Led assessments of WITSML, Pason, Sigmastream data, DrilOps, to evaluate drilling data standards, ensuring seamless integration across Chevron platforms.
• Provided 24/7 support for mission-critical pipelines, resolving latency and ingestion issues within defined SLAs to maintain operational continuity.
• Mentored junior engineers in Azure Data Engineering best practices, fostering a culture of innovation and scalability.

Implemented real-time data engineering solutions for Chevron’s drilling operations, optimising cloud-to-cloud data aggregation.

Designed ETL frameworks for high-frequency data ingestion and storage using Azure Data Engineering stack and Python.

• Collaborated with cross-functional teams to establish data quality and compliance standards, achieving 99.95% uptime.
• Automated QA/QC workflows for data validation and latency monitoring, enhancing operational efficiency.
• Provided round-the-clock support for real-time data delivery, resolving critical issues within defined SLAs.
• Developed channel mapping templates and managed change processes to ensure seamless integration with Chevron’s Data Aggregator.
• Client: Chevron

Managed data migration from legacy systems to Azure data engineering stack, resulting in improved data accessibility and operational efficiency.

• Oversaw data pipeline development, ensuring seamless integration and reliability.
• Utilized technologies including ADB, ADF, Azure SQL, and Python for effective data management.
• Collaborated with backend and data engineering teams to streamline operations between two entities, facilitating smoother data flow and integration.
• Analyzed container movement processes and implemented improvements, reducing discrepancies in invoicing and increasing accuracy.
• Reduced invoice leakage related to container movements through targeted project initiatives.

Supported operations in over 100 countries by optimizing data processes for Maersk Line.

• Defined categories for project scope, including port expenses and container yard basics. into port expenses, container yard basics, and miscellaneous items.

Assessed customer needs and goals through communication and system evaluations to modify existing databases for personalisedcustomisation. Wrote and coded descriptions for physical and logical databases Identified functional and business requirements to meet businessneeds.Developed data models and database designs to plan projects.• Analysed and developed technical and functional specifications.• Wrote scripts and processes for data integration and bug fixes.• Developed data models and database designs.• Responsibilities: Communicate with end users and understand the requirements and gather all information. Collect all the related dataand information related to the use case and application. Apply alldata quality rules on the data and make data in structured format.Build pipeline using ADF, ADLS, BLOB, ADB, Azure Synapse, Python,PySpark, BigData pipeline or orchestration.

Assessed customer needs and goals through communication and
system evaluations to modify existing databases for personalised
customisation. Wrote and coded descriptions for physical and logical databases Identified functional and business requirements to meet business
needs.Developed data models and database designs to plan projects.
• Analysed and developed technical and functional specifications.
• Wrote scripts and processes for data integration and bug fixes.
• Developed data models and database designs.
• Responsibilies: Communicate with end users and understand the
requirements and gather all information. Collect all the related data
and information related to the use case and application. Apply all
data quality rules on the data and make data in structured format.
Build pipeline using ADF, ADLS, BLOB, ADB, Azure Synapse, Python,PySpark, BigData pipeline or orchestration.

PDF file extraction through Acodis a third party tool.
Access the data from different sources. Divide the data from files
according to requirements. Develop query for analysis according to
business requirements with the help of these data frames. Create
unit test scripts for generating coverage report to increase the
performance of the code.

• Created data models and database designs to effectively plan and execute projects, ensuring alignment with business objectives.
• Design, plan, and implement solutions for ingesting data from different applications onto Foundry.
• Understand the business logic and create the data pipeline by performing complex transformations in Pyspark.
• Developed Spark applications using Spark-SQL in Databricks for data extraction, transformation, and aggregation from multiple file formats.
• Developed scripts and processes for data integration and resolving bugs, enhancing system functionality.
• Design automated data pipelines, developed and scheduled job workflows using Foundry Scheduler.
• Created comprehensive data models and designed databases to support project execution.
• Acquired knowledge of essential health checks necessary for maintaining dataset integrity.
• Responsibilities: Communicate with end users and understand the requirements and gather all information. Collect all the related data and information related to the use case and application. Apply all data quality rules on the data and make data in structured format. Build pipeline using PySpark, Python, MCU, GitLab CI/CD
  pipeline. Create PySpark Data frame with the help of these files. Access the data from different sources. Divide the data from files according to requirements. Develop query for analysis according to business requirements with the help of these data frames. Create unit test scripts for generating coverage reports to increase the performance of the code. SonarQ integration of codes to improve code acceptance.
• Identified functional and business requirements to meet business needs.
• Analysed and developed technical and functional specifications.
• Wrote and coded descriptions for physical and logical databases
• Communicated with end users to understand requirements and gathered all necessary information. Collected relevant data related to the use case and application. Applied data quality rules to structure the data correctly. Built pipelines using PySpark, Python, MCU, and GitLab CI/CD pipeline. Created PySpark DataFrames using these files. Accessed data from various sources and divided it based on requirements. Developed queries for analysis according to business requirements using these data frames. Created unit test scripts to generate coverage reports that improved code performance. Integrated SonarQ to enhance code acceptance.
• Created ontology objects on the datasets, and used them in a workshop application.
• Establishing connections from different sources (JDBC, FTP, and API) to Foundry.
• Establishing connections from different sources (JDBC, FTP, and API) to Foundry.

• Designed and implemented ETL processes for multiple clients, enhancing data integrity and operational efficiency.