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Mid-level, generalist Data Engineer role with common big-data skills attracts moderate applicant density.
Core data-engineering skills are broadly transferable, though Azure/Databricks specifics create moderate domain bias.
Explicit 5-8 years plus mandatory big-data, Azure/Databricks and cloud stack makes candidate filtering strict.
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Design and implement big data and analytics solutions directly collaborating with partners and senior client stakeholders.
Develop data solutions within Big Data Azure and/or other cloud environments including Data Architecture using Azure Data Factory, Databricks, Data Lake, and Synapse.
Perform data mapping, integrate, transform, and model data supporting Data Science and Analytics teams with KPI and reporting tools such as Power BI and Tableau.
5-8 years of relevant work experience in data engineering or related roles.
Technical expertise in Python, Spark, Hadoop, Clojure, Git, SQL, Databricks, and visualization tools such as Tableau and PowerBI.
Experience with cloud, container, and microservice infrastructures; hands-on experience with data modelling and query techniques.
Not explicitly mentioned: specific degree, location, or notice period requirements.
Experienced in working with complex, divergent data sets tailored for Data Science and Analytics teams' needs.
Able to collaborate effectively with CTOs, Product Owners, and Operations teams to define and deliver engineering roadmaps.
Familiar with agile methodologies and comfortable working in dynamic, cross-functional teams dealing with technical planning and delivery.