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Popular mid-level data-engineer title plus broad big-data skills increases candidate competition.
Core data engineering skills (Python, SQL, Spark, cloud) are highly transferable across industries.
Explicit 2-4 years plus mandatory big-data and cloud techs raise shortlisting strictness.
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Design and implement big data and analytics solutions for clients, collaborating with partners and senior stakeholders.
Develop data architectures and solutions within Azure and other cloud environments, including Data Factory, Databricks, Data Lake, and Synapse.
Perform data mapping, support KPI development with analytics teams, and maintain documentation and knowledge bases.
2-4 years of experience in data engineering or related roles.
Technical skills in Python, Spark, Hadoop, Clojure, Git, SQL, Databricks, Tableau, and PowerBI.
Experience with cloud platforms, containerization, microservices, and big data technologies, especially Azure.
Work Experience Required: 2-4 years. Notice period: Not explicitly mentioned in the JD.
Able to work independently and communicate effectively with remote and cross-functional teams including CTOs and product owners.
Experienced in handling divergent data sets to meet data science and analytics requirements in agile environments.
Skilled in data modelling, query optimization, and familiar with software development and agile methodologies like Scrum.