Hiring Mode: Full-time with LatentView Analytics

Start Date: ASAP

Experience: 5 to 10 years

Location: San Jose, California, USA

Note: C2C or C2H is not an option.

Job Description:

We are seeking a skilled and experienced Data Engineer to join our dynamic team. As a Data Engineer, you will play a crucial role in designing, implementing, and maintaining data pipelines and systems that enable efficient data processing and analysis. You will collaborate with cross-functional teams to understand business requirements and translate them into scalable data solutions. The ideal candidate will have expertise in project management, client management, SQL, Azure services, ETL pipelines, data validation, and a passion for leveraging data to drive business insights.

Responsibilities:

Management: Lead data engineering projects from inception to completion, ensuring timely delivery and alignment with business objectives. Coordinate with stakeholders to gather requirements, define project scope, and manage resources effectively.
Management: Serve as the primary point of contact for clients, understanding their data needs, and providing technical guidance and support. Build and maintain strong client relationships through effective communication and responsiveness.
Expertise: Design and optimize complex SQL queries for data extraction, transformation, and loading (ETL). Perform advanced data manipulation and analysis to derive actionable insights from large datasets.
Knowledge: Demonstrate proficiency in Azure cloud services, including Azure Data Factory, Azure SQL Database, Azure Databricks, and Azure Storage. Architect and implement scalable data solutions on the Azure platform, adhering to best practices and security standards.
Pipelines: Develop robust ETL pipelines to automate data ingestion, cleansing, and transformation processes. Implement data integration solutions to facilitate seamless data flow across various systems and applications.
Validation: Design and implement data validation strategies to ensure data accuracy, consistency, and integrity. Develop data quality checks and monitoring mechanisms to proactively identify and resolve data issues.
Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver actionable insights. Work closely with cross-functional teams to integrate data solutions into business processes and applications.

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