Data Engineer - AWS

  • Menlo Park, CA
  • Posted 13 hours ago | Updated 13 hours ago

Overview

Hybrid
$50 - $60
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

Amazon Web Services
Cloud Computing
Data Modeling
Data Science
Machine Learning (ML)
Relational Databases
Python
Web Services
Performance Tuning

Job Details

You will:

Lead the architecture, design, delivery & deployment of core data platforms, data warehouse and data modeling needs.

Exceptional understanding on various data topics w.r.t. data engineering i.e. building data pipelines, data modeling , data warehousing etc.

Understanding and experience with cloud software development. Specifically AWS Cloud.

Contribute to the design and execution of data governance, data quality frameworks.

Have a passion and attention to detail for all aspects of data from ingestion, validation/quality, transformation, modeling, storage etc.

Interface with various teams from product, laboratory, web services, data science etc.

Minimum Requirements:

B.S. / M.S. in a quantitative field (e.g. Computer Science, Engineering, Mathematics, Physics, Computational Biology) with at least 6 years of related industry experience, or Ph.D. with at least 4 years of related industry experience

Substantial experience in architecting and delivering secure, scalable cloud-based data warehouses / data lakes on AWS, Azure, or Google Cloud Platform

Exceptional experience with data modeling principles , patterns and industry trends.

Very comfortable in designing and reorganizing facts and dimensions tables, complex data models, SCDs, etc.

Solid object-oriented and/or functional programming experience, specifically in Python and GO.

Expert with data pipelining and workflow engines, like Apache Airflow, Spark etc., and proven ability to choose the correct frameworks as well as tools depending on the requirements.

Experience with provisioning on AWS Cloud, e.g. with Terraform or cloudformation. Leveraging CI in a cloud environment for automation.

In depth Experience with relational databases, query authoring, and performance tuning.

Ability to take a high-level requirement and decompose that into clear engineering objectives, which can be further evolved into detailed specifications.

High emotional quotient to work with potential ambiguity, ask the right questions and engage to drive resolution from requirements to solutions.

The following are highly welcome:

Proven track record of building and operating scalable data infrastructure, managing data models for hundreds to thousands of tables.

Experience with various data products, involvement in build vs buy decisions, designing a solution with limited resources and/or timelines .

Experience with DevOps, e.g. CI/CD pipelines, containerized deployment, infrastructure as code, Terraform.

Experience building microservices and web applications.

Experience with supporting data science / machine learning data pipelines.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.