Akube
Overview
City:
LA, CA Onsite / Hybrid / Remote:
Remote Duration:
6 months Rate Range:
Up to $92.5 / hr on W2 depending on experience (no C2C or 1099 or sub-contract) Work Authorization:
GC, USC, All valid EADs except OPT, CPT, H1B Core Skills:
Expertise in big data engineering pipelines, Spark. Python, MPP Databases / SQL (Snowflake), Cloud Environments (AWS) Must Have Expertise in Big Data engineering pipelines Strong SQL and MPP Databases (Snowflake, Redshift, or BigQuery) Apache Spark (PySpark, Scala, Hadoop ecosystem) Python / Scala / Java programming Cloud Environments (AWS S3, EMR, EC2) Data Warehousing and Data Modeling Data orchestration / ETL tools (Airflow or similar)
Responsibilities
Design, build, and optimize large-scale data pipelines and warehousing solutions. Develop ETL workflows in Big Data environments across cloud, on-prem, or hybrid setups. Collaborate with Data Product Managers, Architects, and Engineers to deliver scalable and reliable data solutions. Define data models and frameworks for data warehouses and marts supporting analytics and audience engagement. Maintain strong documentation practices for data governance and quality standards. Ensure solutions meet SLAs, operational efficiency, and support analytics / data science teams. Contribute to Agile / Scrum processes and continuously drive team improvements.
Qualifications
6+ years of experience in data engineering with large, distributed data systems. Strong SQL expertise with ability to create performant datasets. Hands-on experience with Spark, Hadoop (HDFS, Hive, Presto, PySpark). Proficiency in Python, Scala, or Java. Experience with at least one major MPP or cloud database (Snowflake preferred, Redshift or BigQuery acceptable). Experience with orchestration tools such as Airflow. Strong knowledge of data modeling techniques and data warehousing best practices. Familiarity with Agile methodologies. Excellent problem-solving, analytical, and communication skills. Bachelors degree in STEM required.
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City:
LA, CA Onsite / Hybrid / Remote:
Remote Duration:
6 months Rate Range:
Up to $92.5 / hr on W2 depending on experience (no C2C or 1099 or sub-contract) Work Authorization:
GC, USC, All valid EADs except OPT, CPT, H1B Core Skills:
Expertise in big data engineering pipelines, Spark. Python, MPP Databases / SQL (Snowflake), Cloud Environments (AWS) Must Have Expertise in Big Data engineering pipelines Strong SQL and MPP Databases (Snowflake, Redshift, or BigQuery) Apache Spark (PySpark, Scala, Hadoop ecosystem) Python / Scala / Java programming Cloud Environments (AWS S3, EMR, EC2) Data Warehousing and Data Modeling Data orchestration / ETL tools (Airflow or similar)
Responsibilities
Design, build, and optimize large-scale data pipelines and warehousing solutions. Develop ETL workflows in Big Data environments across cloud, on-prem, or hybrid setups. Collaborate with Data Product Managers, Architects, and Engineers to deliver scalable and reliable data solutions. Define data models and frameworks for data warehouses and marts supporting analytics and audience engagement. Maintain strong documentation practices for data governance and quality standards. Ensure solutions meet SLAs, operational efficiency, and support analytics / data science teams. Contribute to Agile / Scrum processes and continuously drive team improvements.
Qualifications
6+ years of experience in data engineering with large, distributed data systems. Strong SQL expertise with ability to create performant datasets. Hands-on experience with Spark, Hadoop (HDFS, Hive, Presto, PySpark). Proficiency in Python, Scala, or Java. Experience with at least one major MPP or cloud database (Snowflake preferred, Redshift or BigQuery acceptable). Experience with orchestration tools such as Airflow. Strong knowledge of data modeling techniques and data warehousing best practices. Familiarity with Agile methodologies. Excellent problem-solving, analytical, and communication skills. Bachelors degree in STEM required.
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