Data Engineer
New
Los Angeles, CAFull-Time
Salary104,000 - 145,000 USD per year
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Job Details
- Required Skills
- AWSPythonSQLETLGCPHadoopJavaSnowflakeAzureData engineeringNosqlSparkScalaData modelingBigQueryRedshift
Requirements
- Proven experience as a Data Engineer, Data Analyst, or in a similar data-related role.
- Strong knowledge of data engineering principles, data modeling, and data architecture.
- Proficiency with databases (e.g., SQL, NoSQL) and data processing frameworks (e.g., Apache Spark, Hadoop).
- Experience with ETL tools and processes, including data integration and transformation.
- Familiarity with cloud platforms and data storage solutions (e.g., AWS, Azure, Google Cloud).
- Knowledge of programming languages such as Python, Java, or Scala for data processing and scripting.
- Experience with data warehousing technologies (e.g., Redshift, BigQuery, Snowflake) and tools for building data pipelines.
- Strong analytical and problem-solving skills with a focus on optimizing data processes.
- Ability to work in a fast-paced environment and collaborate with cross-functional teams.
- Bachelor’s degree in Computer Science, Engineering, or a related field; advanced degrees or certifications are a plus.
Responsibilities
- Design, build, and maintain scalable and efficient data pipelines that handle large volumes of structured and unstructured data.
- Integrate data from various internal and external sources, ensuring seamless data flow between systems, databases, and storage solutions.
- Develop and manage data warehouses and data lakes, ensuring that data is properly organized, stored, and optimized for analysis.
- Implement and optimize ETL (Extract, Transform, Load) processes to ensure data is transformed and stored correctly for downstream analysis.
- Manage and optimize databases (e.g., SQL, NoSQL) to ensure high performance, availability, and scalability.
- Monitor data pipelines to identify and resolve issues, ensuring high data quality, integrity, and reliability.
- Work closely with data scientists, analysts, and business teams to understand data requirements, provide data access, and ensure alignment with business goals.
- Implement automation strategies to reduce manual intervention and optimize data processing workflows.
- Optimize and fine-tune data processes and queries to enhance performance and reduce latency.
- Document data pipeline processes, workflows, and best practices, ensuring clarity for internal teams and future development.
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