Network Analyst (Data Analyst)

We’re not here to waste your time. Read this carefully and only apply if you’re ready to do real work that matters.

What the job entails:

• You’ll be part of a small, sharp team applying Network Science to real-world business problems inside a high-impact AI exploration unit. We’re turning raw data into meaningful graphs, analyzing connections, detecting hidden patterns, and building tools to help autonomous agents make better decisions.

• You won’t be optimizing dashboards or stuck tweaking marketing reports. You’ll be building intelligence infrastructure.

Your main tasks:

• Transform tabular data into graphs that expose structure and behavior.

• Research, prototype, and deploy graph algorithms to detect communities, centrality, anomalies, and more.

• Build and maintain ETL pipelines for graph data extraction and enrichment.

• Design graph visualizations to support human and machine understanding.

• Collaborate with Data Scientists and LLM Engineers to integrate network-based reasoning into our autonomous systems.

• Experiment often, document clearly, and ship code that matters.

Technologies / techniques used:

• Python (heavy use)

• SQL (PostgreSQL, BigQuery)

• Graph libraries (e.g. NetworkX, cuGraph, Graphistry)

• Visualization tools (Plotly, Dash, D3, etc.)

• Neo4j or other graph databases

• GCP (preferred) or any major cloud provider

• GitHub + CI/CD pipelines

What you'll need:

• Strong Python and SQL skills.

• Ability to break down abstract problems into experimental pipelines.

• Clear communication skills in Portuguese and English.

• Curiosity and initiative. You find answers, you don’t wait for them.

• Solid data wrangling and visualization abilities.

• Willingness to go deep into Network Science, even if you’re not an expert (yet).

Nice to have:

• Experience with NetworkX, Neo4j, or any graph database.

• Previous Experience or openness to learn and work with Elixir codebases

• Background in graph theory, link prediction, community detection.

• Knowledge of probabilistic modeling or LLM integration with graph-based systems.

• Familiarity with GCP and large-scale data pipelines.

Recruiting process outline:

• Open-ended technical case – you’ll analyze a small graph dataset and extract insights.

• Technical interview – discuss your solution and background.

• Cultural interview – aligned expectations, mutual fit.

• If you’re not interested in doing a real technical assessment or engaging in an honest conversation, don’t apply.

Diversity and inclusion:

We believe in social inclusion, respect, and appreciation of all people. We promote a welcoming work environment, where each CloudWalker can be authentic, regardless of gender, ethnicity, race, religion, sexuality, mobility, disability, or education.