Manoj Maharana

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

• Collaborated with cross-functional teams to establish data quality and compliance standards, achieving 99.95% uptime.
• Designed ETL frameworks for high-frequency data ingestion and storage using Azure Data Engineering stack and Python.
• Developed channel mapping templates, managed change processes, and ensured integration with Chevron’s Data Aggregator.
• Automated QA/QC workflows for data validation and latency monitoring, improving operational efficiency.
• Provided round-the-clock support for real-time data delivery, resolving critical issues within defined SLAs.

Managed data migration from legacy systems to Azure data engineering stack, enhancing efficiency.

• Oversaw data pipeline development, ensuring seamless integration and reliability.
• Reduced invoice leakage related to container movements through targeted project initiatives.
• Collaborated with backend and data engineering teams to streamline operations across two entities.
• Utilized technologies including ADB, ADF, Azure SQL, and Python for effective data management.
• Categorized project scope into port expenses, container yard basics, and miscellaneous items.

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

• Analyzed and improved container movement processes to minimize discrepancies in invoicing.

Projects: BAYER and ROCHE

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 fileextraction through Acodis a third party tool.
Access the data from diferent 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.

• Experienced in IQVIA RDS Clinical Trial Data
• 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 PySpark, Python, MCU, GitLab CI/CD
  pipeline. Create PySpark Data frame with the help of these files. Access the data from diferent 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 generang coverage report to increase the performance of the code. SonarQ integration of codes to improve code acceptance.
• 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.
• Involved in developing Spark applications using Spark-SQL in Databricks for data extraction, transformation, and aggregation from multiple file formats.
• Design automated data pipelines, developed and scheduled job workflows using Foundry Scheduler.
• Created ontology objects on the datasets, and used them in a workshop application.
• Knowledge of various health checks required for datasets.
• Establishing connections from different sources (JDBC, FTP, and API) to Foundry.

• Worked as an ETL developer for multiple clients.