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# Senior Data Engineer

- **Company:** Raas Infotek LLC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Big Data, BigQuery, Cloud Computing, Continuous Integration, Data Validation, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Systems, Data Warehousing, DevOps, Distributed Computing Environment, Python (Programming Language), Performance Tuning, SQL Databases, Talend, Google Cloud, Azure Data Factory, Snowflake, Apache Spark, Git, Data Lakes, Pyspark, AWS Glue, Apache Kafka, Data Management, Stream Processing, Data Pipelines, Databricks
- **Published:** October 30, 2026
- **Apply:** https://www.dice.com/job-detail/9c03e24f-ab96-497d-a887-d4be460c941e

## About the Role

* 10+ years of experience in Data Engineering.
 * Strong expertise in SQL, Python, and PySpark.
 * Hands-on experience with ETL tools such as Informatica, Talend, or AWS Glue.
 * Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
 * Strong knowledge of Snowflake, Databricks, BigQuery, or Redshift.
 * Experience with Apache Spark, Kafka, and distributed data processing.
 * Good understanding of data warehousing and data modeling concepts.
 * Excellent problem-solving and communication skills., * Experience with CI/CD, Git, and DevOps practices.
 * Knowledge of Airflow, Azure Data Factory, or similar orchestration tools.
 * Experience with real-time data processing and data governance.
 * Ability to lead technical discussions and mentor junior developers.

## Description

We are seeking an experienced Senior Data Engineer with 10+ years of experience in designing, developing, and maintaining scalable data pipelines and data platforms. The ideal candidate should have strong expertise in ETL/ELT development, cloud technologies, data warehousing, and big data processing., * Design, develop, and maintain scalable ETL/ELT data pipelines.

 * Build and optimize data integration and transformation workflows.
 * Work with large datasets using SQL, Python, and PySpark.
 * Develop and manage data solutions using AWS, Azure, or Google Cloud Platform.
 * Implement data warehouses, data lakes, and lakehouse architectures.
 * Perform data modeling, data validation, and performance tuning.
 * Collaborate with data analysts, architects, and business stakeholders.
 * Ensure data quality, security, governance, and pipeline reliability.