We offer highly qualified analytical consultants and on-demand access to experienced and efficient analytical teams.
Our analytical teams consist of experts who seamlessly integrate into your company’s operations, helping you leverage data while ensuring cost-effectiveness and quality. They also contribute to the maturity of your analytical and digital processes.
With our service, you get more from each subcontracted person. Behind every subcontracted resource, you have a whole team that supports and ensures that the individual delivers results and succeeds. We also handle everything:
Technical Profile and Technological Curiosity: Deep interest in the state of the art of technological implementations, future deployments, and automation, with experience in technological infrastructures (Docker, REST API, batch, ML API, etc.).
Experience in Architecture and Agile Methodologies: Solid experience in scalable and optimized cloud architecture (Azure), along with an understanding of best practices and agile methodologies for data integration (Scrum certification).
Knowledge in Data Governance and DevOps/MLOps: Extensive knowledge in data governance, data security, and model operationalization (DevOps, MLOps), including code versioning with tools like Git and AWS CodePipeline.
Technical Skills and Data Warehousing Experience: Extensive knowledge in programming (SQL, NoSQL, Spark, Python, Pandas, Hadoop) and experience in building Data Warehouses, including analysis, design, ETL, reporting, and dashboarding with tools such as Power BI, Qlik, Tableau, and AWS Quicksight.
Experience in Development and Distributed Processing: Experience in developing enterprise applications (Power Apps, Power Automate, .Net) and handling distributed storage and processing systems (HDFS, Hadoop, Spark, OLAP techniques), with knowledge of AWS components (Redshift, Glue, SageMaker, Athena, Dynamo, Databricks).
Skills in Self-Management, Teamwork, and Security: Ability to focus on results, collaborative work, and agile methodologies, along with experience in information security, code versioning (Git), and DevOps.
Analytical Mindset and Technical Experience: Strong analytical skills with experience in mathematics, statistics, programming, and visualization, including high proficiency in Python, R, Spark, SQL, and AWS tools (SageMaker, Kanbas, StudioLab, Databricks, EMR).
Expertise in Analytics and AI: High experience in using advanced analytics algorithms, Deep Learning, Machine Learning, and Artificial Intelligence, with a deep understanding of business and data handling.
Communication and Teamwork Skills: Proficiency in collaborative work in agile environments (with Scrum certification) and excellent communication skills for effective teamwork.
Technical and Analytical skills: Strong technical and analytical capabilities, focusing on identifying opportunities from data with solid knowledge in the industry, business models, strategies, and KPIs.
Expertise in Data Visualization: Experience and knowledge in visualization tools like QlikSense, Power BI, Tableau, and QuickSight, along with graphic design skills to create impactful visual narratives from data.
Teamwork and Agile Methodologies: Competency in collaborative and agile teamwork, supported by experience and/or certifications in visualization tools.
Analytical and Problem-Solving Skills: Proactivity and attention to detail in problem resolution, along with critical thinking in information management.
Expertise in Data Visualization: Knowledge in visualization tools like Power BI, Tableau, QuickSight, and Qlik, with the ability to summarize large volumes of data clearly and accurately.
Teamwork and Agile Methodologies: Competency in teamwork, with experience in agile methodologies and Design Thinking, supported by Scrum certification.
Leadership in Data Engineering and Team Management: Experience in managing data engineering and architecture teams, defining scopes based on requirements, and assigning tasks, with advanced communication skills and experience in agile environments (Scrum certification).
Technical Expertise and Data Warehousing Experience: Deep knowledge in building Data Warehouses, including analysis, design, ETL, reporting, and dashboarding (Power BI, Tableau, Qlik), along with experience in distributed storage systems (HDFS), distributed processing (Hadoop, Spark), and OLAP techniques.
Advanced Knowledge in Programming and Information Security: High proficiency in programming (SQL, NoSQL, Spark, Glue, Pandas, Hadoop), experience in MLOps and DevOps, and training in engineering, supported by postgraduates, courses, and certifications in data integration, treatment, governance, and information security in Cloud environments (AWS, Azure).
Leadership and Management of Analytical Projects: Experience in project management, from translating ideas into analytical products to managing scope, planning, organization, tracking, and project closure, with a focus on budgeting and tactical and operational coordination.
Skills in Agile Methodologies and Problem Solving: Competency in team work in collaborative and agile environments (Scrum certification), with strong communication skills, problem-solving abilities, and ideation methods for innovation, along with a solid understanding of best practices in data integration.
Technical Knowledge and Academic Background: Curiosity about advanced technological implementations and experience in technological infrastructures, supported by training in administration, statistics, economics, systems engineering, finance, and industrial engineering, as well as postgraduates and specialized courses.
Staff Augmentation allows your company to adjust the size and skills of your teams according to project demands without compromising work quality. This facilitates rapid adaptation to changes in business requirements or workload.
This model provides access to data analytics experts and emerging technologies without lengthy hiring processes. Your company can integrate professionals with the necessary skills, ensuring that technical knowledge is always up-to-date.
With Staff Augmentation, companies reduce costs associated with long-term hiring, such as benefits and training. Additionally, it minimizes financial risks by hiring only for the duration of projects, optimizing investment in talent.
At DataKnow, we use Agile Analytics to quickly find value in a data set and better adapt to our clients’ needs effectively and efficiently.
