Alturait
Data Engineer Exp in Scala and Spark at Sacramento, California
Alturait, Sacramento, California, United States, 95899
NOTE : Need only GC/ USC's
Role: Data Engineer Location: Sacramento, California (Onsite) Duration: Long Term
Job Description: We are looking for an experienced Data Engineer with strong expertise in Scala and Apache Spark to design, develop, and optimize large-scale data processing solutions. The ideal candidate should have hands-on experience building scalable ETL pipelines, managing big data workloads, and ensuring high-performance distributed data operations.
Key Responsibilities: Design and develop scalable data pipelines using Scala and Spark.
Build and optimize ETL workflows for structured and unstructured data.
Tune and troubleshoot Spark jobs for better performance and scalability.
Collaborate with data architects, analysts, and business teams to deliver end-to-end solutions.
Implement data validation, transformation, and quality checks.
Work with big data technologies such as HDFS, Hive, and Kafka.
Ensure data reliability, scalability, and governance in all workflows.
Participate in peer reviews and maintain coding best practices.
Troubleshoot production data pipeline issues and ensure smooth operations.
Required Skills: Strong hands-on experience with Scala and Apache Spark (Spark SQL, Spark Streaming, DataFrames, RDDs).
Proficiency in SQL and data modeling.
Experience with Hadoop ecosystem (Hive, HDFS, YARN).
Solid understanding of data lake and data warehouse architectures.
Familiarity with ETL frameworks, CI/CD pipelines, and data version control.
Experience on cloud platforms (AWS, Azure, or GCP) preferred.
Excellent problem-solving, debugging, and analytical skills.
Nice to Have: Experience with Databricks, Airflow, or Kafka.
Exposure to Python or Java for data engineering.
Understanding of DevOps or DataOps practices.
Role: Data Engineer Location: Sacramento, California (Onsite) Duration: Long Term
Job Description: We are looking for an experienced Data Engineer with strong expertise in Scala and Apache Spark to design, develop, and optimize large-scale data processing solutions. The ideal candidate should have hands-on experience building scalable ETL pipelines, managing big data workloads, and ensuring high-performance distributed data operations.
Key Responsibilities: Design and develop scalable data pipelines using Scala and Spark.
Build and optimize ETL workflows for structured and unstructured data.
Tune and troubleshoot Spark jobs for better performance and scalability.
Collaborate with data architects, analysts, and business teams to deliver end-to-end solutions.
Implement data validation, transformation, and quality checks.
Work with big data technologies such as HDFS, Hive, and Kafka.
Ensure data reliability, scalability, and governance in all workflows.
Participate in peer reviews and maintain coding best practices.
Troubleshoot production data pipeline issues and ensure smooth operations.
Required Skills: Strong hands-on experience with Scala and Apache Spark (Spark SQL, Spark Streaming, DataFrames, RDDs).
Proficiency in SQL and data modeling.
Experience with Hadoop ecosystem (Hive, HDFS, YARN).
Solid understanding of data lake and data warehouse architectures.
Familiarity with ETL frameworks, CI/CD pipelines, and data version control.
Experience on cloud platforms (AWS, Azure, or GCP) preferred.
Excellent problem-solving, debugging, and analytical skills.
Nice to Have: Experience with Databricks, Airflow, or Kafka.
Exposure to Python or Java for data engineering.
Understanding of DevOps or DataOps practices.