Data Engineering Expert @ Nokia Networks

Family Description Analytics (AN) covers the promoting of data science to demystify big data and unlock new business potentials from complex data sources. This covers a huge scope: from classical data gathering and business intelligence, through designing and implementing machine learning, all the way to architecting systems that automate data gathering, analytics, and integrating their outcome into other applications. Determines the foundation for data driven and fact based decision making. Provides the tools and intelligence to formulate strategies and future business development plans. Subfamily Description

• Analytics Data Architecture & Quality (ADA) comprises the design, architecture, and development of scalable data models, test and maintain systems and processes to collect, transform, store, and enable analysis of structured and unstructured data, taking into account considerations related to privacy, security, reliability, and scalability
• Contains curation of data and ensures data quality and integrity
• Covers creation and maintenance of a data dictionary of all key sources and tables
• Comprises documentation of key stakeholders and dependencies as well as the gathering, clarification, and translation of business and data requirements into documentations and conceptual designs from which technical solutions are developed
• Contains advising of clients on appropriate data governance capabilities
• You should have 8-12 years of experience in Data Engineering.

As part of Engineering team, you will:

• Be responsible for all data engineering related development.
• You will be responsible for designing, developing, and maintaining efficient ETL processes that facilitate data extraction, transformation, and loading.
• Design, develop, and implement ETL processes to extract, transform, and load data from various source systems into the target data warehouse.
• Collaborate with stakeholders to gather and understand data requirements, ensuring alignment with business needs.
• Perform data profiling and analysis to identify data quality issues and implement necessary transformations for data cleansing.
• Optimize ETL processes for performance, scalability, and efficiency, considering factors such as data volume, processing time, and resource utilization.
• Create and maintain documentation for ETL processes, including source-to-target mappings, transformation rules, and data lineage.
• Monitor ETL jobs, identify and troubleshoot issues, and ensure timely and accurate data delivery.
• Implement security measures to protect sensitive data during the ETL process and ensure compliance with data governance policies.
• Collaborate and support cross-functional teams, including Devops Engineer, data scientists, and business analysts, to understand data requirements and deliver effective solutions.
• Provide ongoing support and maintenance for ETL processes, addressing issues and implementing enhancements as needed