• Build and maintain robust data pipelines processing large volumes of data • Analysis of large data sets using tools such as Python & SQL • Update and optimize our data platform for speed, scalability and cost • Coordinate with different functional teams to understand and meet their data needs • Develop processes and tools to monitor and analyze model performance and data accuracy • Solve general data-related problems • Setting up new pipelines for the full stream/enrichment/curation process • Upkeep of source code locations • Investigating and utilising ML & AI to improve the cloud offering • Development of junior staff members

Role Description We are looking for native or fluent language speakers to help train AI systems by reviewing and annotating content in their language (Italian). This is a long-term, remote gig. No prior experience is required. On the job training will be provided. You will work on a variety of projects for our client that require native proficiency in your language. This could include but is not limited to: - 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 Benefits - Duration: Minimum 6 months (Long-term Project) - Pay Rate: $4.5 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 Company Description You will be contracted by Careerflow on behalf of one of our clients.

Role Description We are looking for native or fluent language speakers to help train AI systems by reviewing and annotating content in their language (Danish). 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: $8 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

• Design and build scalable ETL/ELT pipelines using both batch and streaming approaches. • Develop ingestion workflows from multiple sources such as databases, APIs, and event streams. • Implement ingestion strategies including full load, incremental load, and CDC. • Orchestrate data workflows using Apache Airflow. • Manage data connectors using Airbyte. • Work with Databricks Lakehouse to build and optimize data processing pipelines. • Write and optimize complex SQL queries for analytics and transformation. • Build modular and testable data models using dbt (staging → intermediate → marts). • Maintain data quality, observability, and reliability across the platform. • Work with AWS services such as S3, Lambda, EC2, IAM. • Containerize data services using Docker and Kubernetes (EKS) when needed. • Document pipelines, data models, and data dictionaries for long-term maintainability.