Cloud Data Engineer: Azure, Snowflake & Databricks Pipelines
Listed on 2026-10-23
-
IT/Tech
Data Engineering, Data Warehousing, Cloud Computing: Infrastructure & Operations, Data Science Manager
Cloud Data Engineer with over 5 years of experience in managing structured and unstructured data, performing deep data analysis, and building scalable data pipelines. The ideal candidate will have hands-on expertise in Azure Data Factory (ADF), Snowflake, Databricks, and Python/PySpark, with a strong understanding of cloud-based ETL processes and data normalization techniques.
Skills Used - ADF, Databricks, Python/ Pyspark, SQL, Snowflake
Minimum of 5+ years of experience as a Cloud Data Engineer.
Hands-on experience with Azure Cloud data tools (ADF, SHIR, Logic Apps, ADLS Gen2, Blob Storage) and Snowflake.
Strong experience in ETL development using SQLServer, SSIS, and T-SQL.
Key Responsibilities
Analyze and organize raw data from diverse sources, including healthcare datasets.
Design and implement ETL pipelines using Azure Data Factory.
Develop and optimize SQL queries, stored procedures, and data models in Snowflake.
Parse and flatten semi-structured data (XML, JSON) using Snowflake and SQL.
Build scalable data systems and pipelines to support analytics and reporting.
Migrate on-premise data workloads to cloud platforms, preferably Snowflake.
Work with Databricks using PySpark, SparkSQL, and Python for data transformation.
Ensure data quality through profiling, validation, and adherence to governance standards.
Collaborate in Agile development environments and contribute to continuous improvement.
Identify opportunities to enhance data architecture and storage efficiency.
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).