Data Migration Consultant
RESPONSBILTIES:
• Develop, construct, test, and maintain data architectures and pipelines.
• Create best-practice Extract, Transform, Load (ETL) frameworks; repeatable and reliable data pipelines that convert data into powerful signals and features.
• Handle raw data (structured, unstructured, and semi structured) and align it into a more usable, structured format that is better suited for reporting and analytics.
• Work with cloud solutions architect to ensure data solutions are aligned with company platform architecture and all aspects related to infrastructure.
• Collaborate with business teams to improve data models that feed business intelligence tools, increasing data
• accessibility and fostering data-driven decision making across the organization.
• Ensure data pipeline architecture will support the requirements of the business.
• Document processes and perform periodic system reviews to ensure adherence to established standards and
• processes.
• Evaluate and advise on technical aspects of open work requests in the product backlog with the project lead.
• Define Cloud infrastructure Reference Architectures for common solution archetypes.
SKILLS & EXPERIENCE REQUIRED:
• Proven experience as a data migration engineer or in a similar role, with a track record of manipulating, processing, and extracting value from large, disconnected datasets.
• Demonstrated technical proficiency with data architecture, databases, and processing large data sets.
• Proficient in Oracle databases and comprehensive understanding of ETL processes, including creating and implementing custom ETL processes.
• Experience with cloud services (AWS, Azure), and understanding of distributed systems, such as Hadoop/MapReduce, Spark, or equivalent technologies.
• Knowledge of Kafka, Kinesis, OCI Data Integration, Azure Service Bus or similar technologies for real-time data processing and streaming.
• Experience designing, building, and maintaining data processing systems, as well as experience working with either a MapReduce or an MPP system.
• Strong organizational, critical thinking, and problem-solving skills, with clear understanding of high-performance algorithms and Python scripting.
• Hands-on experience with data warehouses.
• Demonstrated experience in managing and optimizing data pipelines and architectures.
• Strong understanding of streaming data platforms and pub-sub models.
• In-depth knowledge of data warehousing concepts, including data storage, retrieval, and pipeline optimization.
• Experience with machine learning toolkits, data ingestion technologies, data preparation technologies, and data visualization tools is a plus.
• Excellent communication and collaboration abilities, with the ability to work in a dynamic, team-oriented environment and adapt to changes in a fast-paced work environment.
• Data-driven mindset, with the ability to translate business requirements into data solutions.
• Experience with version control systems e.g. Git, and with agile methodologies/scrum.
• Certifications in related field would be an added advantage (e.g. Google Certified Professional Data Engineer, AWS Certified Big Data, etc.).
EDUCATION:
• A bachelor’s degree in Computer Science, Data Science, Software/Computer Engineering, or a related field.
RESPONSBILTIES:
• Develop, construct, test, and maintain data architectures and pipelines.
• Create best-practice Extract, Transform, Load (ETL) frameworks; repeatable and reliable data pipelines that convert data into powerful signals and features.
• Handle raw data (structured, unstructured, and semi structured) and align it into a more usable, structured format that is better suited for reporting and analytics.
• Work with cloud solutions architect to ensure data solutions are aligned with company platform architecture and all aspects related to infrastructure.
• Collaborate with business teams to improve data models that feed business intelligence tools, increasing data
• accessibility and fostering data-driven decision making across the organization.
• Ensure data pipeline architecture will support the requirements of the business.
• Document processes and perform periodic system reviews to ensure adherence to established standards and
• processes.
• Evaluate and advise on technical aspects of open work requests in the product backlog with the project lead.
• Define Cloud infrastructure Reference Architectures for common solution archetypes.
SKILLS & EXPERIENCE REQUIRED:
• Proven experience as a data migration engineer or in a similar role, with a track record of manipulating, processing, and extracting value from large, disconnected datasets.
• Demonstrated technical proficiency with data architecture, databases, and processing large data sets.
• Proficient in Oracle databases and comprehensive understanding of ETL processes, including creating and implementing custom ETL processes.
• Experience with cloud services (AWS, Azure), and understanding of distributed systems, such as Hadoop/MapReduce, Spark, or equivalent technologies.
• Knowledge of Kafka, Kinesis, OCI Data Integration, Azure Service Bus or similar technologies for real-time data processing and streaming.
• Experience designing, building, and maintaining data processing systems, as well as experience working with either a MapReduce or an MPP system.
• Strong organizational, critical thinking, and problem-solving skills, with clear understanding of high-performance algorithms and Python scripting.
• Hands-on experience with data warehouses.
• Demonstrated experience in managing and optimizing data pipelines and architectures.
• Strong understanding of streaming data platforms and pub-sub models.
• In-depth knowledge of data warehousing concepts, including data storage, retrieval, and pipeline optimization.
• Experience with machine learning toolkits, data ingestion technologies, data preparation technologies, and data visualization tools is a plus.
• Excellent communication and collaboration abilities, with the ability to work in a dynamic, team-oriented environment and adapt to changes in a fast-paced work environment.
• Data-driven mindset, with the ability to translate business requirements into data solutions.
• Experience with version control systems e.g. Git, and with agile methodologies/scrum.
• Certifications in related field would be an added advantage (e.g. Google Certified Professional Data Engineer, AWS Certified Big Data, etc.).
EDUCATION:
• A bachelor’s degree in Computer Science, Data Science, Software/Computer Engineering, or a related field.
