Data Analyst

Data Analyst

Fusemachines is a leading AI strategy, talent, and education services and products provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, United States, Canada, and Dominican Republic and more than 450 full-time employees). Fusemachines seeks to bring its global expertise in AI to transform companies around the world.

We are seeking a talented and experienced Data Analyst with expertise in Tableau, Big Query and Snowflake to join our team, with strong expertise and knowledge in the SAAS domain. The Data Analyst will be responsible for gathering, interpreting, analyzing and visualizing large and complex datasets to provide insights and support data-driven decision-making within the organization.

As a Data Analyst, you will work closely with Product Owners to understand their analytical needs and be part of the main digital projects of the Company.

You will be responsible for the definition of Data Strategies, comprising the identification of critical metrics and KPIs and delivery of actionable insights to relevant decision-makers, using the latest technologies based on the cloud.

You will perform as a data translator, creating a bridge between the technical expertise of data engineers and data scientists and the domain expertise of business teams.

Bachelor's or master's degree in a quantitative field such as statistics, mathematics, or computer science.

• At least 7 years of experience in data analytics, with a focus on business intelligence and data visualization.
• At least 4 years of experience using Tableau to design, build and maintain dashboards and reports, and self-service analytics.
• Proven experience delivering large-scale projects and products for Data and Analytics, as a data analyst.

Highly skilled in digital behavior analysis and obtaining business insights.

Highly skilled in Data Visualization, specifically in Tableau.

Strong SQL skills and experience working with complex data sets, Enterprise Data Warehouse and writing advanced SQL queries. Proficient with Relational Databases (Oracle, SQL Server, MySQL, Postgres, or similar) and NonSQL Databases (Cassandra, MongoDB, Neo4j, etc.).

Good programming Skills in one or more languages such as Python.

Expertise in data cleansing, transformation, and validation.

Good understanding of Data Modeling and Database Design Principles. Being able to design and implement efficient database schemas that meet the requirements of the data architecture to support data solutions.

Strong analytical and problem-solving skills with the ability to translate complex data into actionable insights.

Attention to Detail: Being meticulous and paying attention to detail is critical in data analysis. Small errors or inaccuracies can lead to misleading results, so data analysts should have a keen eye for detail and double-check their work.

Strong understanding of the software development lifecycle (SDLC), especially Agile methodologies.

Knowledge of SDLC tools and technologies, including project management software (Jira or similar), source code management (GitHub, GitLab or similar), CI/CD system (GitHub actions, Jenkins or similar) and binary repository manager (Sonatype Nexus or similar).

Good understanding of Data Quality and Governance, including implementation of data quality and integrity checks and monitoring processes to ensure that data is accurate, complete, and consistent.

Effective negotiation and communication skills to collaborate with cross-functional teams, including business users, data architects, DevOps/DataOps/MLOps engineers, data engineers, data scientists, developers, and operations teams. Essential to convey complex technical concepts and insights to non-technical stakeholders effectively.

Self-motivated with the ability to work well in an agile team.

Possesses strong leadership skills with a willingness to lead, create Ideas, and be assertive.

A willingness to stay updated with the latest services, Data Analytics trends, and best practices in the field.

Spoken and written English (Intermediate-advanced level).

Define and implement Data Strategy: collect, process, share, analyze and commercial action of Digital Projects.

Define and implement Digital Analytics Strategy - at a strategic and technical level.

Gathering and exploring data from various sources such as EDWH, databases, spreadsheets, APIs, and other relevant sources to support business requirements, and creating the corresponding analytical layer.

Reviewing and organizing data to ensure accuracy, consistency, and completeness. This may involve handling missing values, removing outliers, and transforming data into a suitable format for analysis.

Proactively applying statistical techniques and analytical methods to examine data, test hypothesis and identify patterns, trends, relationships and insights that inform business decisions, identifying critical metrics and KPIs, to answer complex key questions from stakeholders with an eye for what drives business performance, to draw meaningful conclusions from data and make data-driven recommendations. This will involve using tools like SQL, Tableau, Python or specialized data analysis software.

Design, build and maintain rich interactive visual representations of data through charts, graphs, and dashboards from multiple data sources to communicate insights effectively to stakeholders. Using tools like Tableau and Python libraries (e.g., Matplotlib, Seaborn).

Summarizing and presenting findings from data analysis in a clear and concise manner. This includes creating reports, slide decks, or presentations to communicate insights and recommendations to non-technical stakeholders.

Ensuring the accuracy, consistency, and integrity of data by performing quality checks and validation procedures. This involves identifying and resolving data discrepancies or errors.

Identifying patterns, trends, and correlations in large datasets to extract meaningful information and support business objectives. This may involve using techniques like clustering, classification, regression, or association analysis.

Identifying and implementing best practices for business intelligence, data visualization, reporting and analysis, building self-service capabilities for business users.

Work in an agile team focused on digital projects.

Working closely with cross-functional teams, such as business analysts, data engineers, data scientists and decision-makers, to understand their requirements, provide analytical support, identify key metrics and contribute to data-driven initiatives to solve business challenges.

Staying updated with industry trends, new analytical techniques, and tools to enhance data analysis capabilities and improve efficiency.

If you are a data-driven individual with strong leadership skills and experience with Tableau, Big Query and Snowflake, we encourage you to apply for this exciting opportunity.

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.