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.
Bachelor’s degree in STEM required.
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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.
Bachelor’s degree in STEM required.
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