Build and maintain scalable ETL/ELT pipelines using Azure Data Factory, Snowflake, dbt, and SQL. Develop data models, transformations, tests, and documentation; ingest data from APIs, databases, files, and enterprise applications; optimize performance; ensure data quality; troubleshoot failures; and support production deployments and monitoring.
This is a remote position.
We are looking for an experienced Data Engineer with strong hands-on experience in Azure Data Factory (ADF), Snowflake, dbt, and SQL. The ideal candidate will be responsible for building and maintaining scalable data pipelines, transforming data, and developing reliable data solutions for analytics and reporting.
- Design, develop, and maintain ETL/ELT data pipelines using Azure Data Factory (ADF).
- Build and optimize data solutions using Snowflake as the cloud data warehouse.
- Develop data transformation models using dbt (Data Build Tool).
- Write complex and optimized SQL queries, stored procedures, views, and transformations.
- Develop data pipelines to ingest data from various sources including APIs, databases, files, and enterprise applications.
- Perform data cleansing, transformation, validation, and reconciliation to ensure data quality and accuracy.
- Implement incremental data loads, scheduling, dependency management, and error handling in ADF.
- Develop scalable and optimized Snowflake data models, tables, views, and SQL transformations.
- Build and maintain dbt models, tests, macros, and documentation.
- Troubleshoot pipeline failures, data issues, and performance problems across ADF, Snowflake, and dbt.
- Work with business analysts, data architects, and other engineering teams to understand data requirements and deliver solutions.
- Support production deployments, monitoring, and ongoing enhancement of data pipelines.
- 4+ years of experience in Data Engineering.
- Strong hands-on experience with:
- Azure Data Factory (ADF)
- Snowflake
- dbt
- Advanced SQL
- Azure Data Factory (ADF)
- Strong understanding of ETL/ELT concepts and data warehousing.
- Experience developing complex data pipelines and data transformations.
- Good understanding of dimensional data modeling, including fact and dimension tables.
- Experience with Snowflake performance optimization and query tuning.
- Hands-on experience with dbt models, tests, incremental models, macros, and Jinja.
- Strong troubleshooting and production support skills.
- Experience with Azure cloud services, particularly Azure Storage, Azure Functions, Databricks, or Key Vault.
- Experience integrating data through REST APIs, JSON, CSV, and relational databases.
- Experience with Python or PySpark is a plus.
- Experience with Git and CI/CD using Azure DevOps or similar tools.
- Understanding of data governance, data quality, and security best practices.
- Experience working with large-scale enterprise data platforms.
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Los Angeles is a global leader in entertainment, so it’s no surprise that many of the biggest players in streaming, digital media and game development call the city home. But the city boasts plenty of non-entertainment innovation as well, with tech companies spanning verticals like AI, fintech, e-commerce and biotech. With major universities like Caltech, UCLA, USC and the nearby UC Irvine, the city has a steady supply of top-flight tech and engineering talent — not counting the graduates flocking to Los Angeles from across the world to enjoy its beaches, culture and year-round temperate climate.
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