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Company Overview
A global technology consultancy specializing in designing, developing, and scaling digital and sustainable products and services.
Senior Azure Data Engineer
YOUR RESPONSIBILITIES:
Design, implement, and maintain robust and scalable data pipelines to ingest, transform, and process structured and unstructured data from various sources.
Build and manage data warehouses and data lakes, implementing efficient data storage and retrieval mechanisms. Design data models that support business requirements and analytics use cases.
Leverage cloud platforms (e.g., AWS, Azure, GCP) to design and deploy scalable data infrastructure, optimizing for performance, cost-effectiveness, and reliability.
Implement data quality checks, validation processes, and data governance frameworks to ensure data accuracy, integrity, and compliance with security standards.
Continuously monitor and optimize data pipelines and infrastructure to improve processing speed, reduce latency, and enhance overall system performance.
Work collaboratively with data scientists, analysts, and other stakeholders to understand their data needs, provide technical expertise, and deliver solutions that meet business objectives.
Support business users by implementing data presentation layer including data visualisation using tools like Tableau, PowerBI etc.
Stay up-to-date with emerging technologies and industry trends in the big data and data engineering space, evaluating and implementing innovative solutions to improve data processing capabilities.
Support the definition and optimization of underlying data infrastructure
WHO YOU ARE:
You hold a Bachelor’s, Master’s, or Ph.D. degree in IT, Information Management, and/or Computer Science with at least 6 years of experience.
Good knowledge of the big data technology landscape and concepts related to distributed storage/computing
Experience with big data frameworks (e.g. Hadoop, Spark) and distributions (Cloudera, Hortonworks, MapR)
Experience with batch & ETL jobs to ingest and process data from multiple data sources
Experience with NoSQL databases (e.g. Cassandra, MongoDB, Neo4J, ElasticSearch)
Experience with querying tools (e.g Hive, Spark SQL, Impala)
Experience with PowerBI
Experience or willingness to go in real-time stream processing, using solutions such as Kafka, AWS Kinesis, Flume, and/or Spark Streaming
Experience or willingness to learn about DevOps and DataOps principles (e.g Infrastructure as Code, automating different parts of the data pipeline)
High-level understanding of Data Science concepts and methodologies (how models are built, trained, and deployed)
You are passionate about technology and continuous learning comes naturally to you
Disclaimer
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No Client Relationship: This company is not a client of SpringCube unless stated.
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