- Proven experience as a data 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.
- 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.).
Evaluation Criteria:
LADBS will review the TOS Responses received and select up to 5 of the most qualified candidates to be interviewed based on a review of the resumes provided and the criteria below.
- Education
- Relevant degree in Computer Science, Engineering, Information Technology, or related field
- Advanced degrees or certifications related to data engineering
- Experience
- Previous work experience with data migration and engineering
- Hands-on experience with data warehouses
- Demonstrated experience in managing and optimizing data pipelines and architectures
- Technical Knowledge
- Strong understanding of streaming data platforms and pub-sub models
- In-depth knowledge of data warehousing concepts, including data storage, retrieval, and pipeline optimization