• Migrar dados de sistemas legados para a arquitetura atual, entendendo sistemas complexos que você nunca viu antes — usando IA para acelerar a compreensão e execução. • Estruturar e manter data lake em AWS (S3), organizando camadas de dados (bronze, silver, gold) com governança e qualidade. • Construir e manter pipelines de dados robustos usando Python e serviços AWS (Step Functions, Kinesis, ECS, Lambda, DynamoDB, etc). • Usar IA de forma intensiva no dia a dia: para escrever transformações, entender sistemas desconhecidos, acelerar debug e criar automações. • Criar automações e workflows com IA para acelerar processos do time (ex: scripts de transformação, validação de dados, geração de documentação). • Elevar o nível de uso de IA no time — compartilhar técnicas, prompts e workflows que funcionam, disseminando essa cultura internamente. • Participar do ciclo completo de desenvolvimento, com autonomia para propor soluções e executar.

• Lead Azure Purview Implementations • Architect and deliver end-to-end Microsoft Purview solutions across enterprise environments • Design and configure Data Map, scanning strategies, Metadata ingestion, and classification frameworks • Establish scalable patterns for cataloging, discovery, and governance automation • Define Data Governance Strategy • Design and implement enterprise data governance operating models • Build & Integrate Data Pipelines using Azure Data Factory (ADF), dbt, Databricks, and Alteryx • Develop Python-based solutions for workflow automation, API integrations, and data operations • Collaborate with analytics teams to define semantic layers and business logic in dbt • Ensuring governance is embedded in data pipelines • Conduct governance maturity assessments and define transformation roadmaps • Document data pipelines, models, and governance policies

Role Description We are looking for native or fluent language speakers to help train AI systems by reviewing and annotating content in their language (Indonesian). This is a long-term, remote gig. No prior experience is required. On the job training will be provided. What You Will Do - Review content in your native language (e.g., newspaper articles, website screenshots, advertisements) - Draw bounding boxes around specific elements as instructed (e.g., highlight the commercial aspect of an article) - Answer structured questions about the content - Write short summaries when required - Follow clear annotation guidelines to ensure quality and consistency Qualifications - Native or highly fluent speakers of the target language - Students, freelancers, part-time workers, or anyone seeking flexible short-term work - People comfortable following written instructions on a computer Requirements - Strong reading comprehension in your language - Basic computer skills (no technical background needed) - Attention to detail - Ability to follow structured instructions Role Details - Duration: Minimum 6 months (Long-term Project) - Pay Rate: $2.45 USD / hour - Hours: 30–40 hours/week - Location: Fully remote - Work: Not voice-based — text and image annotation only - No Interview, only assessment: 15–20 minute language skills assessment Process - Please fill out this job application form - Sign up at https://data-labeling.careerflow.ai - Take the Indonesian Language Assessment: https://data-labeling.careerflow.ai/assessments/2aa0b21a-0c4d-4830-9e6e-dd88de7d3b10 - You will be contracted by Careerflow on behalf of one of our clients.

• Architect and contribute directly to core platform components, including ingestion pipelines, transformation frameworks, data models, and orchestration • Define and evolve the multi-quarter technical roadmap for the data platform, balancing new capabilities, reliability investments, and technical debt reduction in alignment with the broader platform architecture • Drive evaluation and adoption of tooling across the stack, ensuring choices are well-reasoned and aligned with where the platform needs to go • Lead architecture reviews and design discussions, ensuring decisions are well-reasoned, documented, and understood by the team • Cut through ambiguity by asking the right questions early about data quality, schema evolution, and downstream dependencies, and identify risks before they become crises • Translate complex data infrastructure decisions for non-technical stakeholders without oversimplifying, and break vague product requirements into clear engineering tasks and acceptance criteria • Partner closely with data science leads and cross-functional teams to surface dependencies and constraints early and prioritize improvements that unlock productivity • Run a lightweight but effective backlog and planning process, keeping the team focused and unblocked • Mentor and grow engineers with an emphasis on raising technical depth — delegate meaningful work, pair on hard problems, and create opportunities for others to stretch • Set code review standards, testing philosophy, and engineering best practices that make the whole team better, including data validation, pipeline testing, and schema management • Ensure data systems work reliably in production — instrumented, observable, and operable, with clear SLAs on freshness, completeness, and accuracy