Cloud Data Engineer

TechDigital Group; Phoenix (AZ); On-site; USD 90,000 - 120,000; Full time

Job summary

A leading technology company is seeking a Cloud Data Engineer with over 5 years of experience in managing structured and unstructured data. The candidate should have hands-on expertise in Azure Data Factory, Snowflake, and Databricks. Responsibilities include analyzing data, designing ETL pipelines, and ensuring data quality. This role involves working in Agile environments and migrating on-premise data to cloud platforms like Snowflake.

Qualifications

• Over 5 years of experience as a Cloud Data Engineer.
• Hands-on experience with Azure data tools and Snowflake.
• Strong experience in ETL development using SQLServer, SSIS, and T-SQL.

Responsibilities

• Analyze and organize raw data from diverse sources.
• Design and implement ETL pipelines using Azure Data Factory.
• Develop and optimize SQL queries and data models in Snowflake.
• Ensure data quality through profiling and validation.
• Collaborate in Agile development environments.

Skills

ADF

Databricks

Python/PySpark

SQL

Snowflake

Job description

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