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Job Description
Structured overview of role & requirementsAbout This Role
Automate and industrialize data pipeline build and development tasks within a Big Data platform and Data Lake environment.
Lead stakeholder discussions and own end-to-end data engineering lifecycle including requirements gathering, data mapping, system design, development, deployment, and operational support.
Provide innovative solutions to complex business problems and maintain data platform post-implementation with Run Management activities.
Minimum Requirements
5-8 years of experience in building data pipelines and data engineering.
Strong proficiency with Big Data ecosystem tools such as Hadoop, Spark, NiFi, Hive, SQL/HQL, Pyspark, and Databricks.
Experience in Azure Data Engineering stack including Azure Data Factory, Event Hubs, IoT Hub, Data Lake Storage Gen2, Azure Databricks, and Azure Synapse Analytics.
Good understanding of DevOps tooling (Kubernetes, Jenkins, Gitlab), programming in Python/Java/Scala, and cloud architecture (preferably Azure).
Ideal Candidate Profile
Experienced in handling large volumes of both structured and unstructured data using architectures like Lambda, Kappa, and Medallion.
Skilled in data integration patterns, API concepts (REST, microservices), and knowledgeable in Agile methodologies (Scrum, Kanban) and tools like JIRA.
Familiar with data modelling, cloud-native data engineering, and open digital architecture principles; preferably certified in Azure or other hyperscaler clouds.