Data visualization • sunnyvale ca
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The average salary range is between $ 71,175 and $ 187,500 year , with the average salary hovering around $ 120,750 year .
The average salary range is between $ 82,499 and $ 153,216 year , with the average salary hovering around $ 105,000 year .
Title : Data Engineer
Location: Santa Clara, CA95051
Duration: 6 Months
Onsite/Hybrid/Remote: Hybrid
Job Description: Data Engineer
We are seeking a skilled Data Application Engineer to design, build, and maintain data-driven applications and pipelines that enable seamless data integration, transformation, and delivery across systems.
The ideal candidate will have a strong foundation in software engineering, database technologies, and cloud data platforms, with a focus on building scalable, robust, and efficient data applications.
Key Responsibilities:
Develop Data Applications: Build and maintain data-centric applications, tools, and APIs to enable real-time and batch data processing.
Data Integration: Design and implement data ingestion pipelines, integrating data from various sources such as databases, APIs, and file systems.
Data Transformation: Create reusable ETL/ELT pipelines to process and transform raw data into consumable formats using tools like Snowflake, DBT, or Python.
Collaboration: Work closely with analysts, and stakeholders to understand requirements and translate them into scalable solutions.
Documentation: Maintain comprehensive documentation for data applications, workflows, and processes.
Required Skills and Qualifications:
Education: Bachelor s degree in Computer Science, Engineering, or a related field (or equivalent experience).
Programming: Proficiency in programming languages Python, C# , ASP.NET (Core)
Databases: Strong understanding of SQL, database design, and experience with relational (e.g., Snowflake, SQL Server) databases
Data Tools: Hands-on experience with ETL/ELT tools and frameworks such as Apache Airflow (DBT - Nice to Have)
Cloud Platforms: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud, and their data services (e.g., S3, AWS Lambda etc.).
Data Pipelines: Experience with real-time data processing tools (e.g., Kafka, Spark) and batch data processing.
APIs: Experience designing and integrating RESTful APIs for data access and application communication.
Version Control: Knowledge of version control systems like Git for code management.
Problem-Solving: Strong analytical and problem-solving skills, with the ability to troubleshoot complex data issues.
Preferred Skills:
Knowledge of containerization tools like Docker and orchestration platforms like Kubernetes.
Experience with BI tools like Tableau, Power BI, or Looker.
Soft Skills:
Excellent communication and collaboration skills to work effectively in cross-functional teams.
Ability to prioritize tasks and manage projects in a fast-paced environment.
Strong attention to detail and commitment to delivering high-quality results.
