Hiring - AWS Data Engineer - Torrance, California

Job Role: AWS Data Engineer

Location :  Torrance, California (Hybrid)

Job Type: W2 Contract 

Top Skills' Details

1 AWS Services including Glue, Redshift, EMR, and Lambda
2 Python & SQL (Client is only seeking SQL and Python rather than Spark Python and Spark SQL)
3 Communication with customer

 Job Description

This team is building out a data lake related to Honda vehicles service demand. This team is tracking when service lights appear on Honda vehicles. They are using the data to be predictive for when customers will need service, with a goal of having these customers seek out Honda dealers and service providers as opposed to 3rd party service providers. They currently have one individual supporting this data environment and need an engineer to mature the data lake. This person will also be working closely with business partners. The technical environment is AWS, Python, and SQL. This person must have great experience with AWS services like Glue, EMR, Airflow, Redshift, EC2, and Lambda.

Additional Skills & Qualifications

• Data Engineer is responsible for building and maintaining the organization's data infrastructure. This role involves designing, implementing, and managing robust and scalable data pipelines to collect, store, and process large volumes of data efficiently.
• The Data Engineer plays a crucial role in data availability and quality to support data analytics and business intelligence efforts within the organization.

KEY ACCOUNTABILITIES
• Data Pipeline Development:
§ Design, develop, and maintain scalable ETL (Extract, Transform, Load) processes for data integration from various sources into the data warehouse or data lake.
§ Ensure code is reviewed with Enterprise ETL Governance team
§ Set up job notifications to be emailed to support groups.
§ Trigger emails to business stakeholders for job delays if we don’t meet SLA
• Data Quality and Management:
• Ensure high levels of data quality, integrity, and availability by implementing data validation and cleaning techniques.
• Monitor and troubleshoot data pipeline issues to ensure smooth and timely data flow.
• Data Infrastructure:
• Develop and manage the underlying data architecture, including databases, data warehouses, and big data platforms.
• Optimize data storage and retrieval for performance and cost efficiency.
• Collaboration:
• Work closely with data analysts, data scientists, and business stakeholders to understand data requirements and deliver actionable insights.
• Participate in code reviews and share best practices with team members.
• Documentation and Compliance:
§ Document data processes, data flows, and data models to ensure transparency and maintainability.
§ Ensure compliance with data governance policies and regulatory requirements.