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Last Updated: 25 Oct 2023

- 10+ years experience working in the IT industry; - 8+ years experience working with Python; - Strong skills with SQL; - Good abilities working with R and C++; - Deep knowledge of AWS; - Experience working with Kubernetes (K8s), and Grafana; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Experience working with Amazon S3, Athena, EMR, Redshift; - Specialised in Data Science and Data Analysis; - Work experience as a team leader; - Upper-Intermediate English.

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AWS Athena

AWS Athena


Python   8 yr.

Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Danila, DevOps Engineer

Last Updated: 19 Dec 2023

DevOps engineer with a Computer Science and Software Engineering background and 3 years of cloud, automation, and infrastructure experience within healthcare and mobile technology domains. Expertise includes AWS cloud services, containerization with Docker and Kubernetes, and IaC with Terraform and Ansible. Proven ability in employing CI/CD pipelines, scripting with Bash and Python, and infrastructure monitoring using the ELK stack. Committed to continuous learning and applying IaC methodologies to enhance resource management and workflow automation.

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AWS Athena

AWS Athena


Terraform   1 yr.


Ansible   1 yr.

Docker Compose

Docker Compose   3 yr.

Kubernetes (K8s)

Kubernetes (K8s)   1 yr.

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Yaroslav M., Scala Software Engineer with Cloud & Data Engineering skills

Ternopil, Ukraine
Last Updated: 4 Jul 2023

- Professional engineer with proven ability to develop efficient solutions for complex problems, including cloud and Data projects; - Microservice architecture expertise Lightbend Reactive Architecture, Infrastructure as Code expertise in AWS CloudFormation, CI/CD (Gitlab, AWS CodePipeline), Cloud expertise - AWS; -Engineer with the ability to develop efficient solutions for complex problems, including cloud projects, AWS Services (Amazon Quicksight, EC2, S3, Glue), Databricks, Kinesis; - API development RESTful, Swagger, GraphQL, API Gateway, Microservice architecture expertise - Commercial experience in IT since 2013; - Lightbend Reactive Architecture, Infrastructure as Code expertise in AWS CloudFormation, CI/CD (Gitlab, AWS CodePipeline); - System level programming, OOP and OOD, functional programming; Stress on profiling and optimizing code, writing reliable code; - System-level programming, OOP and OOD, functional programming; - Profiling and optimizing JVM code; - Experience with product documentation and supporting products; - Upper-intermediate English; - Available ASAP.

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AWS Athena

AWS Athena





Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Amit, Expert Data Engineer

Last Updated: 4 Jul 2023

- 8+ year experience in building data engineering and analytics products (Big data, BI, and Cloud products) - Expertise in building Artificial intelligence and Machine learning applications. - Extensive design and development experience in AZURE, Google, and AWS Clouds. - Extensive experience in loading and analyzing large datasets with Hadoop framework (Map Reduce, HDFS, PIG and HIVE, Flume, Sqoop, SPARK, Impala), No SQL databases like Cassandra. - Extensive experience in migrating on-premise infrastructure to AWS and GCP clouds. - Intermediate English - Available ASAP

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AWS Athena

AWS Athena

Apache Hadoop

Apache Hadoop

Apache Kafka

Apache Kafka

Google Cloud Platform (GCP)

Google Cloud Platform (GCP)

Amazon Web Services (AWS)

Amazon Web Services (AWS)

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Natig, Data Engineer

Last Updated: 14 Jul 2023

- 12+ years experience working in the IT industry; - 12+ years experience in Data Engineering with Oracle Databases, Data Warehouse, Big Data, and Batch/Real time streaming systems; - Good skills working with Microsoft Azure, AWS, and GCP; - Deep abilities working with Big Data/Cloudera/Hadoop, Ecosystem/Data Warehouse, ETL, CI/CD; - Good experience working with Power BI, and Tableau; - 4+ years experience working with Python; - Strong skills with SQL, NoSQL, Spark SQL; - Good abilities working with Snowflake and DBT; - Strong abilities with Apache Kafka, Apache Spark/PySpark, and Apache Airflow; - Upper-Intermediate English.

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AWS Athena

AWS Athena


Python   4 yr.

Microsoft Azure

Microsoft Azure   5 yr.

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Table of Contents

Soft skills of a AWS Athena Developer

Soft skills are essential for an AWS Athena Developer as they contribute to effective communication, collaboration, and problem-solving abilities in a professional environment.


  • Adaptability: Ability to quickly learn and adapt to new technologies and tools.
  • Teamwork: Collaboration and teamwork skills to work effectively with other team members.
  • Problem-solving: Analytical thinking and problem-solving skills to identify and resolve issues.
  • Communication: Strong verbal and written communication skills to convey ideas and information clearly.
  • Time Management: Effective time management skills to prioritize tasks and meet deadlines.


  • Leadership: Ability to take on leadership roles and guide junior team members.
  • Mentoring: Willingness to mentor and support the development of junior team members.
  • Client Management: Strong client-facing skills to understand and address client requirements.
  • Conflict Resolution: Excellent conflict resolution skills to resolve issues and maintain team harmony.
  • Critical Thinking: Strong critical thinking skills to analyze complex problems and find innovative solutions.
  • Attention to Detail: Strong attention to detail to ensure accuracy and quality in all tasks.
  • Presentation Skills: Ability to deliver effective presentations and explain technical concepts to non-technical stakeholders.


  • Strategic Thinking: Ability to think strategically and align technical solutions with business goals.
  • Project Management: Strong project management skills to plan, execute, and deliver projects successfully.
  • Negotiation: Excellent negotiation skills to achieve mutually beneficial outcomes.
  • Decision Making: Strong decision-making skills to make informed choices based on data and analysis.
  • Innovation: Ability to drive innovation and identify opportunities for process improvements.
  • Collaboration: Proven track record of collaborating with cross-functional teams and stakeholders.
  • Empathy: Ability to understand and empathize with the needs and perspectives of team members and clients.
  • Continuous Learning: Willingness to continuously learn and stay updated with the latest technologies and industry trends.

Expert/Team Lead

  • Strategic Leadership: Ability to provide strategic direction and lead teams towards achieving business objectives.
  • Team Management: Proven experience in managing and inspiring teams to achieve high performance.
  • Change Management: Ability to lead and manage organizational change effectively.
  • Business Acumen: Strong business acumen to understand the impact of technical decisions on the overall business.
  • Stakeholder Management: Excellent stakeholder management skills to build and maintain strong relationships.
  • Influence: Ability to influence and persuade stakeholders to adopt new technologies or approaches.
  • Problem Solving: Expert problem-solving skills to address complex technical challenges.
  • Risk Management: Proven ability to identify and mitigate risks associated with technical projects.
  • Strategic Partnerships: Ability to build strategic partnerships with external vendors and organizations.
  • Thought Leadership: Recognized as a thought leader in the field, contributing to industry knowledge and best practices.
  • Communication: Exceptional communication skills to effectively convey complex technical concepts to diverse audiences.

Let’s consider Difference between Junior, Middle, Senior, Expert/Team Lead developer roles.

Seniority NameYears of experienceResponsibilities and activitiesAverage salary (USD/year)
Junior Developer0-2 years– Assisting senior developers in coding and testing
– Learning and implementing new technologies
– Debugging and troubleshooting software issues
– Collaborating with team members on project tasks
Middle Developer2-5 years– Developing and maintaining software applications
– Participating in system design and architecture discussions
– Mentoring junior developers
– Conducting code reviews and ensuring quality standards
– Collaborating with cross-functional teams
Senior Developer5-8 years– Leading software development projects
– Designing and implementing complex software solutions
– Providing technical guidance and mentorship to the team
– Conducting code refactoring and optimization
– Collaborating with stakeholders to define project requirements
Expert/Team Lead Developer8+ years– Leading a team of developers
– Setting technical direction and making architectural decisions
– Managing project timelines and deliverables
– Mentoring and coaching team members
– Collaborating with clients and stakeholders on project requirements

How and where is AWS Athena used?

Case NameCase Description
Ad-hoc Data AnalysisAWS Athena allows users to perform ad-hoc data analysis on large datasets stored in Amazon S3 without the need for complex data processing systems. Users can run SQL queries directly on their data in S3, enabling them to explore and analyze the data quickly and efficiently. This is particularly useful in scenarios where organizations need to gain insights from their data in near real-time or need to perform on-the-fly analysis for decision-making purposes.
Log AnalysisAWS Athena can be leveraged for analyzing log data generated by various applications and systems. By querying log files stored in S3 using SQL, users can gain insights into system performance, identify anomalies, and troubleshoot issues. For example, an e-commerce company can use Athena to analyze web server logs to understand user behavior, identify patterns, and optimize their website’s performance.
Clickstream AnalysisClickstream data provides valuable insights into user behavior on websites or mobile applications. AWS Athena can be used to analyze clickstream data stored in S3, allowing organizations to understand user navigation patterns, identify popular pages or features, and optimize user experiences. This information can help businesses make data-driven decisions to improve customer engagement and conversion rates.
Data Lake QueryingAs part of a data lake architecture, AWS Athena can serve as a powerful querying tool. Data lakes store vast amounts of structured and unstructured data, and querying this data efficiently is crucial. Athena enables users to query data directly from their data lake in S3, without the need for data transformation or loading it into a separate data warehouse. This saves time and resources, making data lakes more accessible for analysis and exploration.
ETL WorkflowsAWS Athena can be integrated into Extract, Transform, Load (ETL) workflows, allowing users to perform data transformations and prepare data for downstream processing or analysis. By leveraging Athena’s SQL capabilities, users can filter, aggregate, and manipulate data stored in S3 before loading it into other systems or data warehouses. This helps streamline data pipelines and automate data processing tasks, improving overall data workflow efficiency.

What are top AWS Athena instruments and tools?

  • AWS Glue: AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analysis. It automatically generates ETL code to transform raw data into a format that can be queried using AWS Athena. It was launched in 2017 and is widely used for data preparation and transformation tasks.
  • AWS CloudTrail: AWS CloudTrail is a service that provides governance, compliance, operational auditing, and risk auditing of your AWS account. It captures API activity and delivers log files to an Amazon S3 bucket. The logs can be easily queried using AWS Athena to gain insights into user activity, resource usage, and changes made to your AWS environment. AWS CloudTrail was introduced in 2013 and has become a crucial tool for auditing and monitoring AWS environments.
  • AWS Glue Data Catalog: The AWS Glue Data Catalog is a fully managed metadata repository that stores metadata information about data sources, transformations, and targets. It integrates with AWS Athena to provide a central location for storing and managing metadata. The AWS Glue Data Catalog was introduced in 2017 and has gained popularity as a reliable and scalable metadata management solution.
  • AWS Lambda: AWS Lambda is a compute service that lets you run code without provisioning or managing servers. It can be used in conjunction with AWS Athena to automate data processing and analysis tasks. By triggering Lambda functions based on events or schedules, you can perform complex data transformations and aggregations before querying the data using AWS Athena. AWS Lambda was launched in 2014 and has become a popular tool for serverless computing.
  • AWS CloudFormation: AWS CloudFormation is a service that helps you model and set up your AWS resources so you can automate the deployment and management of your infrastructure. It can be used to create and manage the AWS Athena resources required for querying and analyzing data. AWS CloudFormation was introduced in 2011 and has become a standard tool for infrastructure as code.
  • AWS Glue DataBrew: AWS Glue DataBrew is a visual data preparation tool that makes it easy for non-technical users to clean and transform data for analysis. It provides a visual interface to perform data cleansing, normalization, and other data preparation tasks. The transformed data can be directly queried using AWS Athena for analysis. AWS Glue DataBrew was launched in 2020 and has gained popularity for its simplicity and ease of use.
  • AWS Athena Workgroups: AWS Athena Workgroups is a feature that allows you to manage and organize your query execution in AWS Athena. It enables you to set fine-grained access control, query execution settings, and result location for different workloads or user groups. By using AWS Athena Workgroups, you can optimize query performance and resource allocation. AWS Athena Workgroups was introduced in 2019 and has become an essential tool for workload management in AWS Athena.
  • AWS Glue Studio: AWS Glue Studio is a visual interface for creating, running, and monitoring AWS Glue ETL jobs. It provides a drag-and-drop interface to build ETL workflows and supports data transformation through a variety of built-in transformations. AWS Glue Studio simplifies the process of data preparation and transformation for use with AWS Athena. It was launched in 2021 and has received positive feedback for its ease of use and visual workflow capabilities.
  • AWS CloudWatch: AWS CloudWatch is a monitoring and observability service that provides data and actionable insights for AWS resources and applications. It can be used to monitor the performance and health of AWS Athena queries by capturing and analyzing metrics, logs, and events. AWS CloudWatch was introduced in 2009 and has become a standard tool for monitoring AWS environments.

TOP 10 Facts about AWS Athena

  • AWS Athena is a serverless interactive query service that allows you to analyze data directly in Amazon S3 using standard SQL.
  • With Athena, you don’t need to set up and manage complex ETL processes or data warehouses. You can simply create a table schema and start querying your data instantly.
  • Athena supports a wide range of data formats, including CSV, JSON, Parquet, Avro, and ORC, making it flexible and compatible with various data sources.
  • It leverages the power of distributed computing and automatically scales to handle large datasets, allowing you to process petabytes of data without any upfront infrastructure provisioning.
  • Athena provides fast query execution times by utilizing a distributed query engine called Presto, which is optimized for running SQL queries on large datasets.
  • You only pay for the queries you run, with no upfront costs or long-term commitments. This cost-effective pricing model makes Athena suitable for both small-scale and enterprise-level data analysis.
  • Athena integrates seamlessly with other AWS services, such as Amazon QuickSight for visualization, AWS Glue for data cataloging, and AWS Lambda for serverless data processing, enabling you to build end-to-end data analytics pipelines.
  • It offers fine-grained access control using AWS Identity and Access Management (IAM) policies, allowing you to manage and restrict user access to specific data tables and columns.
  • Athena provides built-in support for query result caching, which helps to improve query performance and reduce costs by reusing previously computed results.
  • With its easy-to-use interface and familiar SQL syntax, Athena empowers analysts, data scientists, and developers to quickly gain insights from their data and make data-driven decisions.

Cases when AWS Athena does not work

  1. Poorly structured or unoptimized data: AWS Athena works best with data that is stored in a well-structured and optimized format, such as Apache Parquet or Apache ORC. If your data is stored in a format that is not suitable for querying, Athena may not be able to efficiently process your queries.
  2. Large datasets without proper partitioning: Partitioning your data in Athena allows you to optimize query performance by reducing the amount of data that needs to be scanned. If your datasets are large and not properly partitioned, Athena may struggle to provide fast query results.
  3. Complex or resource-intensive queries: While Athena is capable of handling complex queries, there may be cases where extremely complex or resource-intensive queries exceed the capacity of the underlying infrastructure. In such cases, you may experience slow query performance or even query failures.
  4. Insufficient concurrency limits: By default, AWS Athena enforces certain concurrency limits to prevent abuse and ensure fair resource allocation. If your workload requires a higher level of concurrency, you may need to request a limit increase or consider alternative solutions.
  5. Unsupported data formats or data types: Although Athena supports a wide range of data formats and data types, there may be cases where your specific data format or data type is not supported. It is important to ensure that your data is compatible with Athena’s supported formats and types.
  6. Connectivity or network issues: AWS Athena is a cloud-based service, and its performance can be influenced by factors such as network latency or connectivity issues. If you are experiencing consistent connectivity problems, it may impact the overall functionality of Athena.

TOP 10 AWS Athena Related Technologies

  • Python

    Python is a widely used programming language that is known for its simplicity and readability. It has a large ecosystem of libraries and frameworks, making it a popular choice for software development with AWS Athena. Python can be used to write Athena queries, automate tasks, and build data pipelines.

  • SQL

    SQL (Structured Query Language) is a standard language for managing and manipulating relational databases. It is essential for working with AWS Athena as it allows developers to write queries to retrieve and analyze data stored in S3. SQL is easy to learn and widely used in the industry.

  • Amazon S3

    Amazon S3 (Simple Storage Service) is an object storage service offered by AWS. It is a fundamental component for working with AWS Athena as it provides a scalable and durable storage solution for the data that Athena queries. S3 is highly reliable and provides low-latency access to data.

  • AWS Glue

    AWS Glue is a fully managed extract, transform, and load (ETL) service provided by AWS. It is commonly used with AWS Athena to catalog and prepare data for analysis. Glue can automatically discover the schema of data stored in S3 and create Athena tables, saving development time.

  • AWS CloudFormation

    AWS CloudFormation is an infrastructure as code service that allows developers to define and provision AWS resources in a declarative manner. It can be used to create and manage the necessary resources for setting up an AWS Athena environment, including S3 buckets, IAM roles, and Athena workgroups.

  • Jupyter Notebook

    Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, visualizations, and explanatory text. It is often used for interactive data exploration and analysis with AWS Athena. Jupyter Notebook supports various programming languages, including Python and SQL.

  • AWS SDKs

    AWS Software Development Kits (SDKs) provide libraries and APIs for various programming languages to interact with AWS services. Using the AWS SDKs, developers can easily integrate AWS Athena into their applications and automate tasks such as query execution, result retrieval, and data manipulation.

Pros & cons of AWS Athena

8 Pros of AWS Athena

  • Serverless: AWS Athena is a serverless query service, which means you don’t have to provision or manage any infrastructure. This eliminates the need for capacity planning and reduces operational overhead.
  • Scalability: Athena automatically scales to accommodate any query load, allowing you to run queries on large datasets without performance degradation.
  • Pay per Query: With AWS Athena, you only pay for the queries you run. There are no upfront costs or long-term commitments. This pay-per-query pricing model offers cost-effective usage for sporadic or unpredictable query workloads.
  • Integration with AWS Services: Athena seamlessly integrates with other AWS services like Amazon S3, Glue, and AWS Lake Formation. This makes it easy to query data stored in different formats and locations within your AWS ecosystem.
  • SQL Compatibility: Athena supports standard SQL, allowing you to use familiar SQL syntax and functions to query your data. This makes it accessible to users with SQL knowledge and reduces the learning curve.
  • Fast Results: Athena uses massively parallel processing (MPP) to distribute queries across a large number of nodes. This enables fast query execution and provides quick results, even on large datasets.
  • Schema Flexibility: Athena offers schema-on-read functionality, allowing you to query data without the need for predefined schemas. This provides flexibility in handling structured, semi-structured, and unstructured data.
  • Data Partitioning and Compression: Athena supports data partitioning and compression techniques, which can significantly improve query performance and reduce storage costs.

8 Cons of AWS Athena

  • Query Performance: While Athena provides fast query execution, the performance may vary based on the complexity of the query and the size of the dataset. Highly complex queries or queries on very large datasets may experience longer execution times.
  • Data Format Limitations: Athena works best with columnar data formats like Apache Parquet and ORC. While it can query other formats like CSV and JSON, performance may be impacted due to the lack of columnar storage and compression.
  • Incremental Data Updates: Athena is optimized for querying static data stored in Amazon S3. If your data is frequently updated or requires real-time analysis, you may need to integrate additional tools or processes to handle incremental data updates.
  • Data Transfer Costs: When using Athena, data transfer costs may apply if your data is stored in a different region than the Athena query execution location. These costs should be considered when planning your overall budget.
  • Data Privacy and Security: As with any cloud service, it’s essential to ensure proper data privacy and security measures are in place. This includes managing access control, encryption, and compliance with relevant regulations.
  • Learning Curve: While SQL compatibility makes Athena accessible to SQL users, there may still be a learning curve for those unfamiliar with AWS services and the specific nuances of querying data in a serverless environment.
  • No Real-time Processing: Athena is primarily designed for ad-hoc query analysis and batch processing. If you require real-time data processing or streaming analytics, other AWS services like Amazon Kinesis or AWS Glue Streaming may be more suitable.
  • Limited Control over Infrastructure: Since Athena is a serverless service, you have limited control over the underlying infrastructure. This may restrict your ability to fine-tune performance optimizations or customize certain aspects of the service.

TOP 10 Tech facts and history of creation and versions about AWS Athena Development

  • AWS Athena was launched in November 2016 as a serverless interactive query service for analyzing data in Amazon S3 using standard SQL.
  • It was developed by Amazon Web Services (AWS), one of the leading cloud computing providers in the world.
  • Athena is based on Presto, an open-source distributed SQL query engine developed by Facebook.
  • With Athena, users can run ad-hoc queries on large datasets stored in S3 without the need for infrastructure provisioning or data loading.
  • It supports various data formats including CSV, JSON, Apache Parquet, and Apache ORC.
  • Athena uses a pay-per-query pricing model, allowing users to pay only for the amount of data scanned by their queries.
  • In 2018, AWS announced support for running Athena queries in parallel, significantly improving query performance for large datasets.
  • Athena integrates with AWS Glue, a fully managed extract, transform, and load (ETL) service, enabling users to define and manage their data catalogs.
  • It provides an easy-to-use web interface as well as a command-line interface (CLI) for interacting with the service.
  • Since its launch, AWS has continued to enhance Athena with new features and performance improvements based on customer feedback.

Hard skills of a AWS Athena Developer

Hard skills of an AWS Athena Developer:


  • SQL: Proficiency in writing SQL queries to extract and manipulate data from large datasets.
  • AWS Athena: Basic understanding of AWS Athena and its query execution capabilities.
  • Data Modeling: Knowledge of data modeling techniques to design efficient and scalable Athena tables.
  • Data Formats: Familiarity with various data formats like CSV, JSON, and Parquet for querying in Athena.
  • Data Partitioning: Understanding of data partitioning strategies to optimize query performance in Athena.


  • Performance Optimization: Experience in optimizing query performance using techniques like query tuning and indexing.
  • ETL Processes: Proficiency in designing and implementing ETL processes to transform and load data into Athena.
  • Database Administration: Understanding of database administration tasks like managing schemas, tables, and permissions in Athena.
  • Data Security: Knowledge of implementing and maintaining data security measures in Athena, including encryption and access control.
  • Data Integration: Familiarity with integrating Athena with other AWS services like S3, Glue, and Redshift for seamless data workflows.
  • Monitoring and Troubleshooting: Ability to monitor and troubleshoot query execution errors and performance issues in Athena.
  • Data Governance: Understanding of data governance principles and best practices for maintaining data quality and compliance in Athena.


  • Advanced SQL: Expertise in writing complex SQL queries involving subqueries, joins, and window functions for advanced data analysis.
  • Query Optimization: Proven track record of optimizing complex queries through query plan analysis and performance tuning techniques.
  • Data Lake Architecture: Deep understanding of data lake architectures and the role of Athena in building scalable and cost-effective data processing pipelines.
  • Data Pipeline Automation: Experience in automating data pipelines using AWS Glue or other ETL tools to orchestrate data ingestion and transformation in Athena.
  • Data Governance Frameworks: Knowledge of implementing data governance frameworks and frameworks like Apache Ranger for enforcing data access policies in Athena.
  • Data Cataloging: Proficiency in setting up and maintaining a data catalog using AWS Glue or similar tools for efficient data discovery and metadata management in Athena.
  • Serverless Computing: Expertise in leveraging serverless computing capabilities of AWS Athena for cost optimization and scalability.
  • Performance Monitoring: Ability to implement performance monitoring and alerting mechanisms to proactively identify and resolve performance bottlenecks in Athena.

Expert/Team Lead

  • Data Lake Architecture Design: Extensive experience in designing and implementing end-to-end data lake architectures using Athena as a key component.
  • Big Data Technologies: Proficiency in other big data technologies like Apache Spark or Presto for advanced data processing and analytics in Athena.
  • Data Governance Strategy: Ability to define and execute a comprehensive data governance strategy for an organization using Athena.
  • Cloud Cost Optimization: Expertise in optimizing cloud costs by implementing cost-effective data storage and query optimization techniques in Athena.
  • Performance Benchmarking: Experience in conducting performance benchmarking and capacity planning exercises for Athena to ensure optimal system performance.
  • Team Leadership: Strong leadership skills and experience in leading and mentoring a team of Athena developers and data engineers.
  • Client Management: Ability to effectively communicate and collaborate with clients to understand their business requirements and provide appropriate solutions using Athena.
  • Continuous Improvement: Proven track record of driving continuous improvement initiatives and implementing best practices in Athena development and operations.
  • Industry Knowledge: Deep understanding of industry trends and emerging technologies related to data analytics and cloud computing in the context of Athena.
  • Problem Solving: Exceptional problem-solving skills to troubleshoot complex issues and provide innovative solutions in the Athena environment.
  • Project Management: Proficiency in project management methodologies and tools to successfully deliver Athena projects within scope, timeline, and budget.

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Maria Lapko
Global Partnership Manager