he data engineering role will be responsible for implementing data solution for ad-hoc requests from end-to-end including but not limited to requirement gathering, gap analysis of the existing data platform and deploying the solution into the production environment. This role also includes coordinating with the Devops team and functional leads to ensure conformity to existing standards.
Lead the design, architecture, and implementation of as-hoc data initiatives and platform capabilities while ensuring alignment with business needs and scalability requirements.
Enhance existing data workflows and systems to improve performance, cost-efficiency, to implement innovative solutions.
Lead cross-functional projects, collaborating with product managers, analysts, solutions architects, and other stakeholders to define requirements, prioritize tasks, and deliver high-quality solutions.
Communicate effectively with non-technical stakeholders to understand business needs, translate them into technical specifications, and present technical concepts in a clear and concise manner.
4+ years of hands-on experience in data engineering, including data lake infrastructure, data warehousing, and data analytics tools.
Cloud data engineering experience on Azure. Deep understanding of Azure services like Data Factory, Databricks, and Machine Learning. Azure certification is a plus.
Strong proficiency in SQL, Python and PySpark. Proven ability to optimize SQL queries and performance tune data pipelines.
Experience in building and operating highly available, distributed data pipelines for large-scale data ingestion, processing, and extraction.
Experience integrating multi-cloud services with on-premises technologies. Strong understanding of data modeling processes.
Proven ability to solve complex data problems, collaborate effectively with cross-functional teams, and deliver high-quality solutions.
Nice to have skill set include:
Agile experience
Dataiku
Power BI
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