Data Engineer

Key Skills

AWS, Continuous Integration & Continuous Delivery - CI/CD, Data Lake, Data management, Data pipelines, Data security, Data Warehousing, Design, DevOps, ETL/ELT technologies, Lakehouse, Marketo, NoSQL, Oracle, PaaS, PySpark, Python Programming, SaaS, Salesforce, Scrum Agile Methodology

Job Description

Responsible for performing difficult or complex analysis, design and programming involving multi-project leadership and broad responsibility in support of new and existing Data Engineering Projects and production support activities.

Core Duties

• Collaborate with IT and business partners to devise a data strategy that caters to Stanford requirements.
• Deep understanding and commitment to software engineering principles/processes (e.g. Lean, Agile, DevOps, CI/CD) and continuous improvement through measurement
• Thorough knowledge, expertise, and practice Data Management Framework to design world class data stores. Best practices, Data Quality and security are critical.
• Understand data endpoints, consumers and develop strategy.
• Fluid end-to-end data vision, design pipelines for seamless data flow.
• Lead and perform the design, development, implementation and maintenance of complex Data Store/ Data Lake/Lake house and Data warehousing systems and data-intensive solutions that are scalable, optimized, and fault-tolerant.
• Design and implement Data Migration and Data Integration across cloud and hybrid environments.
• Mastery and hands-on experience with Data Engineering technologies and scripting languages. Identify new technologies and provide recommendations to Management.
• Solid understanding and experience in Cloud technologies and applications. Data Migration, Integration, API’s development, Data Streaming (Batch and continuous) and scheduling.
• Data Modeling skills. Able to come up with a Canonical Data Model and simplify data flow and interaction between different applications. Should be able to integrate new sources smoothly.
• Ability to translate complex functional and technical requirements into detailed architecture, design and high performing software.
• Design, build and optimize pipelines for data collection for storage, access and analytics.
• Out of the box thinking to overcome engineering challenges with innovative design principles. 

Minimum Requirement

Education and Experience:

• Bachelor's degree in scientific or analytic field and five years relevant experience or a combination of education and relevant experience.

Knowledge, Skills and Expertise:

• Thorough understanding and experience in Data Lake, Lake House and Data Warehousing Architecture. Should be able to suggest, architect and implement Data Lake/Lake house/ Data Warehouse solution with a set of available cloud tools and programming.
• Hands-on experience and expertise in Advanced SQL, Advanced Python programming, AWS Services (such as S3, Redshift, Glue ETL, IAM, DMS, Appflow, VPC and others), SnowFlake, FiveTran, Informatica , KAFKA, Airflow, Oracle Cloud and other open source tools.
• Experience in writing reusable complex Python/PySpark scripts for ELT, Business Logic OR APIs. Other Coding experience such as Scala and R Programming is a plus.
• Hands-on development work on all aspects of data analysis, data provisioning, modeling, performance tuning and optimization.
• Experience in working on AWS cloud environment, using the marketplace for the right tool, efficient utilization of them to meet business requirements.
• Mastery of relational, NoSQL or NewSQL database systems. Expertise in working with unstructured, structured, and semi-structured data.

• Build scalable data pipelines for both real time and batch using best practices in data modeling, ETL/ELT processing using various technology stack
• Experience in designing and implementing tight data security at various levels.
• Experience in streaming data from SaaS/PaaS applications - SalesForce, ServiceNow, Workday, Oracle Cloud, Marketo and others.
• Experience with DataOps and related set of practices, processes and technologies.
• Experienced in Data Migration and Data Integration. Know the pain points in Data integration across SaaS applications and implement the best solution that fits the organization.
• Constantly monitor operations, tune for better performance and utilization. 

At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website (https://cardinalatwork.stanford.edu/benefits-rewards) provides detailed information on Stanford’s extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process.

The expected pay range for this position is $142,000 to $160,000 per annum. Stanford University provides pay ranges representing its good faith estimate of what the university reasonably expects to pay for a position. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs.

PHYSICAL REQUIREMENTS*:

• Constantly perform desk-based computer tasks.
• Frequently sit, grasp lightly/fine manipulation.
• Occasionally stand/walk, use a telephone.
• Rarely writing by hand, lift/carry/push/pull objects that weigh up to 10 pounds.

* Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of the job.