AWS Lambda is a serverless computing service that runs code in response to events and automatically manages the underlying compute resources. It allows users to execute code without provisioning or managing servers, scaling automatically from a few requests per day to thousands per second.

About AWS Lambda
AWS Lambda was introduced by Amazon Web Services in 2014. It was created to simplify the execution of code in response to events without requiring users to manage server infrastructure. The service aimed to provide scalable and cost-effective computing by charging only for the compute time consumed.
Strengths of AWS Lambda include automatic scaling, cost efficiency, and ease of integration with other AWS services. Weaknesses involve cold start latency, limited execution duration, and potential complexity in debugging. Competitors include Google Cloud Functions, Microsoft Azure Functions, and IBM Cloud Functions.
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How to hire a AWS Lambda expert
An AWS Lambda expert must have skills in serverless architecture, proficiency in programming languages like Python, Node.js, or Java, experience with AWS services such as S3, DynamoDB, and API Gateway, and expertise in monitoring and logging using CloudWatch. They should also understand IAM roles and permissions for secure access management.

Adam C.
Skills
A highly skilled professional with a Bachelor's degree in Information Systems, specializing in Data Engineering and Software Development, complemented by extensive experience in project management and data analytics. Demonstrated expertise in building robust data lakes utilizing cutting-edge technologies such as AWS and Python, along with a strong background in database management and ETL processes. Proficient in transforming raw data into actionable insights through advanced analytical techniques and visualization tools like Power BI and Amazon QuickSight. Possesses a solid foundation in agile methodologies, enabling effective collaboration across cross-functional teams. Committed to continual professional development in Data Science and Artificial Intelligence, showcasing a proactive approach to adopting innovative solutions that enhance organizational efficiencies and decision-making.

Luiz F.
Skills
A data scientist with four years of industry experience and a proven track record in machine learning research and development, this candidate holds a PhD in Computer Engineering. The expertise encompasses a diverse range of machine learning techniques, including regression, classification, and clustering. Demonstrated proficiency in designing ensemble models for industrial applications has led to publications and enhancements in preventive maintenance strategies. Previous roles include backend development and the creation of a scalable data architecture utilizing AWS for data lakes, showcasing strong skills in feature engineering, ETL automation, and big data solutions. The ability to communicate complex results through storytelling and use case driven insights is leveraged to guide informed business decision-making.

Mateus T.
Skills
This candidate is a seasoned professional specializing in cloud computing, microservices, and serverless architectures, with a strong proficiency in AWS technologies such as Lambdas, API Gateway, and AppSync. Demonstrated expertise includes leading the development of sophisticated systems like Evocities, an intelligent city management platform, and Evoview, a machine learning-based video analysis tool. Equipped with advanced knowledge in machine learning, this individual excels at integrating artificial intelligence into diverse projects, enhancing functionality and performance.

Naoki T.
Skills
With over 15 years of entrepreneurial experience and a strong specialization in data leadership, this candidate exemplifies exceptional analytical capabilities. Proficient in the complete data science and engineering workflow, they excel in extract, transform, load (ETL) processes, as well as production implementation. Expertise encompasses programming languages such as Python, R, and JavaScript, alongside a focus on developing supervised and unsupervised machine learning models, including neural networks. Adept in agile methodologies like Scrum, Kanban, and Crisp-DM, they apply quality management practices such as Ishikawa diagrams and SWOT analysis. The candidate possesses extensive knowledge of both relational and non-relational database systems (SQL and NoSQL), particularly MongoDB and Elasticsearch, coupled with advanced skills in processing large data volumes using Spark and PySpark. Cloud infrastructure expertise spans across AWS and Azure services, including S3, EC2, Lambda, and security measures. A notable profile also includes proficiency in Natural Language Processing (NLP) and recent experiences with large language models (LLM), equipping them to tackle complex challenges in the data sector. They also conduct technical interviews and provide team training, showcasing leadership in both technical and collaborative environments.
*Estimations are based on information from Glassdoor, salary.com and live Howdy data.
USA
$ 224K
Employer Cost
$ 127K
Employer Cost
$ 97K
Benefits + Taxes + Fees
Salary
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