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Senior Data Engineer

Raas Infotek LLCPlano, TX🇺🇸United States

Why this role stands out

This hybrid Senior Data Engineer role at Raas Infotek LLC offers a fantastic opportunity to leverage your extensive experience with Python, SQL, and cloud platforms to build impactful data solutions. You'll thrive here if you possess a deep understanding of data warehousing, ETL/ELT, and distributed processing, and are eager to contribute to a dynamic tech environment. Apply today to advance your career in data engineering!

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationPlano, TX, United States
DockerMySQLOracleSQLGitApache SparkAWSETLSnowflakeTableauAirflowApacheBigQueryDatabricksGoogle CloudHadoopHiveKafkaPythonRedshiftAzurePostgreSQLKubernetesPower BISQL ServerCI/CDAzure Data Factory

Job description

Location: Texas, USA
Experience: 11+ Years
Job Type: W2
Relocation: Candidates willing to relocate are welcome.

Job Summary

We are looking for an experienced Senior Data Engineer with strong hands-on experience in data engineering, ETL pipelines, cloud platforms, and big data technologies. The candidate will be responsible for building scalable data solutions, integrating data from multiple sources, and ensuring data quality and reliability.

Required Technologies

  • Programming: Python, SQL, PySpark.

  • Data Engineering: ETL/ELT, data pipelines, data modeling, data warehousing.

  • Big Data: Apache Spark, Hadoop, Hive, Kafka.

  • Cloud Platforms: AWS, Azure, or Google Cloud Platform.

  • Data Platforms: Snowflake, Databricks, Amazon Redshift, or Google BigQuery.

  • ETL Tools: AWS Glue, Azure Data Factory, Informatica, or similar tools.

  • Databases: SQL Server, Oracle, PostgreSQL, or MySQL.

  • Workflow Orchestration: Apache Airflow or similar tools.

  • DevOps: Git, CI/CD, Docker, and Kubernetes.

  • Data Quality: Data validation, data cleansing, monitoring, and performance optimization.

  • Reporting: Power BI or Tableau is a plus.

Responsibilities

  • Design, develop, and maintain scalable data pipelines.

  • Build and optimize ETL/ELT processes using Python, SQL, and PySpark.

  • Integrate data from databases, APIs, and other sources.

  • Develop and maintain data warehouses and data models.

  • Monitor data quality and troubleshoot pipeline issues.

  • Optimize data processing performance and reliability.

  • Collaborate with data analysts, data scientists, and business teams.

Preferred Domain Experience

Experience in one or more of the following domains is preferred:

  • Banking and Financial Services

  • Healthcare

  • Insurance

  • Retail and E-commerce

  • Telecommunications

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