Cloud Data Engineer (Snowflake/Databricks)
New
J
JobgetherData Engineering
IndiaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- AWSPythonSQLApache AirflowGCPSnowflakeAzureSparkDatabricks
Requirements
- 4+ years of professional experience in Data Engineering or a closely related field.
- Strong proficiency in SQL and Python, with experience applying both to production-grade data engineering solutions.
- Hands-on experience working with Snowflake and/or Databricks.
- Practical experience with Apache Spark, including batch and/or streaming data processing.
- Proven experience designing and implementing ETL/ELT pipelines.
- Familiarity with Apache Airflow or similar data orchestration technologies.
- Experience working with at least one major cloud platform, including AWS, Azure, or Google Cloud Platform (GCP).
- Strong understanding of data modeling principles and experience designing scalable analytical data structures.
- Knowledge of data quality, governance, monitoring, and reliability practices.
- Experience with dbt or comparable data transformation tools is preferred.
- Experience with real-time streaming technologies such as Kafka, Kinesis, or Pub/Sub is an advantage.
- Familiarity with BI tools and downstream analytics use cases is a plus.
Responsibilities
- Design, develop, and maintain scalable ETL/ELT data pipelines that reliably process and transform large volumes of data.
- Build, optimize, and maintain data transformation workflows using Snowflake and/or Databricks.
- Develop effective data modeling strategies, including star schemas, lakehouse architectures, and other scalable approaches.
- Optimize query performance, data processing efficiency, and cloud infrastructure costs.
- Implement and maintain workflow orchestration using Airflow or comparable orchestration technologies.
- Develop reliable datasets and data products that support analytics, business intelligence, and reporting teams.
- Establish and maintain data quality, governance, monitoring, and reliability practices across data pipelines and platforms.
- Work with batch and streaming data processing technologies to support evolving data requirements.
- Collaborate with analytics, BI, engineering, and other cross-functional stakeholders to understand requirements and deliver effective data solutions.
- Continuously improve data platform architecture, pipeline reliability, performance, scalability, and operational efficiency.
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