Data Engineer

Required Skills & Experience

• 3-5 years of experience in data engineering or a related field.

• Strong expertise in SQL, data modeling, and data warehouse concepts.

• Hands-on experience with Azure Data Services (Azure Data Factory, Azure Synapse, Azure Databricks, etc.).

• Proficiency in Snowflake and its ecosystem, including performance tuning and optimization.

• Experience with data streaming technologies such as Apache Kafka, Spark Streaming, or Azure Event Hubs.

• Strong programming skills in Python, Scala, or Java for data processing.

• Knowledge of data governance, security, and compliance best practices.

• Ability to work in agile environments and collaborate effectively with cross-functional teams.

Nice to Have Skills & Experience

• Experience with orchestration tools like Apache Airflow or Azure Data Factory.

• Familiarity with CI/CD pipelines and DevOps practices in data engineering.

• Knowledge of machine learning pipelines and MLOps is a plus.

• Experience working with Big Data technologies such as Hadoop, Spark, or Delta Lake.

Job Description

The ideal candidate will have extensive experience in data warehouse concepts, streaming data processing, Azure cloud services, Snowflake, and SQL. This role will be responsible for designing, building, and optimizing data pipelines, ensuring efficient data management and accessibility to support business intelligence and analytics.

Design, develop, and maintain data pipelines and ETL processes for structured and unstructured data.

• Implement and optimize data warehouse solutions using Snowflake and Azure technologies.

• Work with real-time data streaming technologies such as Apache Kafka, Azure Event Hubs, or similar.

• Develop and optimize SQL queries for data transformation, aggregation, and analysis.

• Ensure data integrity, security, and governance by implementing best practices.

• Collaborate with cross-functional teams to understand data needs and deliver efficient solutions.

• Monitor, troubleshoot, and improve performance of existing data pipelines and systems.

• Stay up to date with industry trends and emerging technologies in data engineering.