Functional Responsibilities:

• Build responsive and interactive web applications using React and TypeScript, focusing on performance, usability, and component-driven UI architecture.
• Design and implement RESTful or GraphQL APIs using Python frameworks such as FastAPI, Django, or Flask.
• Develop and optimize database schemas (PostgreSQL, MySQL, or NoSQL) and ensure smooth data integration between backend, AI, and frontend systems.
• Collaborate with AI/ML engineers to integrate Generative AI components—such as LLM APIs, RAG architectures, and agent-driven workflows—into the application.
• Deploy and maintain applications on cloud platforms (AWS preferred, but Azure or GCP experience is also welcome) using containerization and CI/CD pipelines.
• Use AI coding tools and assistants to accelerate development, debugging, and rapid iteration of production features.
• Write and maintain unit and integration tests for both frontend and backend using tools like Jest, pytest, or Playwright.
• Ensure quality and reliability through basic observability, including structured logging and error monitoring.
• Work closely with designers, product managers, and stakeholders to translate product ideas into high-quality, scalable solutions.
• Stay current with emerging technologies in web development and AI, specifically regarding LLM orchestration and development workflow optimizations.

Qualifications:

• 5+ years of experience as a Full-Stack Engineer, working across both frontend and backend systems.
• Strong proficiency in Python and experience with frameworks such as FastAPI, Django, or Flask.
• Solid experience with JavaScript/TypeScript and React, including modern state management (e.g., Redux, Zustand, or Context API).
• Comfortable designing and consuming RESTful or GraphQL APIs and integrating with third-party services.
• Hands-on experience with SQL/NoSQL and familiarity with data modeling and performance optimization.
• Hands-on experience with LLM integration (e.g., Claude, OpenAI, or Gemini) in production applications.
• Proficiency with AI coding assistants and a clear understanding of how to integrate them into a professional development workflow.
• Working knowledge of cloud platforms (AWS, Azure, or GCP), including deployment and basic infrastructure management.
• Experience with CI/CD pipelines, testing frameworks, and version control (Git).
• Strong problem-solving and collaboration skills, with a proactive and curious mindset.
• Excellent communication skills in English, both written and verbal.

We are seeking a high-impact Data Engineering Manager to drive the technical evolution and operational success of our Data Engineering practice. In this multifaceted role, you will bridge the gap between business development and technical execution, joining client calls to architect robust solutions and identify the ideal talent profiles to see them through. You will act as a strategic steward of our engineering talent, managing bench workload, defining clear performance goals, and overseeing the professional growth of your team through rigorous technical mentorship.

As a cultural ambassador for our Center of Excellence, you will operate with an owner mentality to increase organizational efficiency. This involves assessing top-tier talent, curating content for accelerated upskilling programs, and proactively iterating on internal processes. By merging solution scoping with hands-on talent development, you will ensure our engineering output consistently meets our high bar for quality while driving superior delivery for our clients

Functional Responsibilities:

• Join calls with potential clients to sell our Data Engineering capabilities and understand which types of profiles they are after
• Scope solutions to solve a given business problem presented by a client
• Handle & monitor workload for engineers on the bench
• Craft upskilling programs based on available content
• Mentor engineers on how to deal with technical challenges
• Run and socialize performance reviews for the engineers lead
• Assist engineers on goal definition and progress tracking
• Be a cultural ambassador for the company and the center of excellence
• Interview and assess technical skills in the Data Engineering field
• Help either test or curate content to speed up the content creation process for training purposes
• Proactively propose improvements to our current processes in order to increase efficiency

Qualifications:

• 7+ years of total professional experience, with a consistent track record of professional growth and increasing responsibility.
• 5+ years of experience in specialized data roles, such as Data Engineering or Analytics Engineering, with deep domain expertise.
• 2+ years of experience in leadership roles, demonstrating the ability to manage high-performing teams or lead complex, multi-stakeholder projects.
• Advanced English proficiency, with exceptional written and verbal communication skills tailored for both technical and executive audiences.
• Mastery of data fundamentals, able to design high level data architectures and explain the relationship between each component and their purpose.
• Proven track record of execution, with a demonstrated ability to deliver high-impact results in fast-paced or ambiguous environments.
• Strong project management expertise, including proficiency in agile methodologies, resource planning, and risk mitigation.
• Experience in mentoring and coaching, with a focus on developing technical talent and fostering a culture of continuous learning.
• Strategic problem-solving skills, with a high degree of rigor in measuring outcomes and ensuring data integrity.
• Exceptional stakeholder management skills, acting as a bridge between technical execution and business strategy.

As a Senior Machine Learning Engineer in Computer Vision, you will design and deliver advanced vision systems that power mission-critical applications for global and Fortune 500 companies. You’ll work across deep learning, large-scale data pipelines, and high-performance infrastructure, owning models end-to-end from experimentation to production deployment.

This role is designed for engineers who think systems-level, understand the real-world constraints of ML at scale, and can turn ambiguous visual problems into high-impact, production-ready solutions. You’ll shape architectures, guide model strategy, and bring modern vision capabilities into enterprise environments where reliability, speed, and accuracy matter.

Functional Responsibilities:

• Develop and fine-tune models for tasks like image classification, object detection, segmentation, and generative modeling using TensorFlow, PyTorch, or Keras.
• Implement techniques such as resizing, normalization, data augmentation, and feature extraction to improve model performance.
• Optimize and deploy computer vision models on cloud platforms (AWS, GCP, Azure), edge devices, and specialized hardware (GPUs, TPUs).
• Use CI/CD, model versioning, and monitoring tools to ensure reliable and scalable deployment of vision models.
• Improve model speed and performance using quantization, pruning, and hardware acceleration techniques.

Qualifications:

• +5 years of hands-on experience developing and deploying machine learning models in production environments. 
• Proven experience writing production-level code, with strong proficiency in Python.
• Strong Python programming skills with proficiency in deep learning frameworks (TensorFlow, PyTorch, or Keras).
• Expertise in designing, training, and fine-tuning models for: Image classification (ResNet, EfficientNet), Object detection (Faster R-CNN, YOLO, SSD) or Image segmentation (U-Net, Mask R-CNN).
• Strong understanding of image preprocessing techniques (resizing, normalization, data augmentation).
• Experience with computer vision libraries such as OpenCV and torchvision.
• Experience with transfer learning and adapting pre-trained models.
• Ability to deploy models on cloud platforms (AWS, GCP, Azure) and specialized hardware (GPUs, TPUs).
• Familiarity with MLOps tools for automating ML pipelines.

We are seeking a talented and experienced Enterprise Key Account Director to join our team! You'll have the opportunity to take the lead in managing and growing our account. You'll work closely with our valued customers to understand their business needs and objectives and develop strategies to ensure their continued satisfaction. We're looking for a relationship-building superstar passionate about delivering outstanding results and creating long-lasting partnerships. If this sounds like you, we'd love to hear from you!

Functional Responsibilities:

• Own and strengthen executive-level relationships with strategic clients, including C-suite stakeholders, ensuring long-term partnership success and high satisfaction across multiple business units.
• Drive strategic account expansion, including identifying new opportunities within existing clients, developing tailored growth strategies, and leading the proposal and negotiation process to secure additional business.
• Oversee and monitor client satisfaction through structured check-ins, executive business reviews, and data-driven insights, implementing initiatives to enhance retention, loyalty, and account maturity.
• Lead and coordinate cross-functional teams to resolve escalations and complex challenges promptly, ensuring high-quality service delivery and minimizing operational impact for clients.
• Assess clients’ evolving business needs and proactively recommend additional engineers, solutions, or service lines that align with their strategic roadmap.
• Collaborate with Sales, Operations, and Delivery leadership to design and execute strategic plans that drive account growth, resource allocation, and long-term commercial outcomes.
• Serve as the primary executive point of contact, ensuring seamless communication and alignment between client leadership and Factored’s internal stakeholders.
• Own account performance metrics, leveraging data to forecast growth, identify risks, and create strategic roadmaps that maximize revenue and client value.
• Provide coaching and direction to engineers, analysts, and internal teams by sharing client insights and feedback to continuously improve product quality and service delivery.

Qualifications:

• 8+ years of experience in strategic account management, enterprise sales, customer success, or related fields.
• Proven track record of managing and growing large, complex, or enterprise-level accounts.
• Strong strategic mindset with the ability to think beyond day-to-day operations and design long-term growth plans.
• Exceptional relationship-building and negotiation skills, with experience engaging senior executives and influencing key stakeholders.
• Advanced analytical, problem-solving, and decision-making skills, using data to guide strategies and recommendations.
• Demonstrated leadership abilities, including influencing cross-functional teams and driving outcomes without direct authority.
• Excellent organizational and prioritization skills, with the ability to manage multiple high-impact accounts simultaneously.
• Outstanding communication skills in English, both written and verbal, with the ability to present to executive audiences.

We are currently looking for an exceptionally talented Senior Data Scientist - Time Series Specialist to join our team and help build cutting-edge solutions at the intersection of manufacturing and advanced analytics. In this role, you will design and deploy scalable ML systems that detect anomalies, uncover root causes, and transform complex manufacturing and test log data into actionable insights that drive product quality and operational efficiency.

Functional Responsibilities:

• Design and implement ML and statistical models for anomaly detection on large-scale manufacturing test logs and machine outputs.
• Work with high-volume time series and structured machine log data, meeting low-latency requirements in production environments.
• Build both real-time (live line) detection pipelines and post-hoc failure analysis and root cause analysis workflows.
• Translate domain expertise into scalable, production-ready systems, ensuring reliability, observability, and performance.
• Continuously evaluate and apply emerging techniques in machine learning and time series analysis to improve model performance and robustness.

Qualifications:

• 4+ years of experience in Data Science, Machine Learning, or a related field.
• Hands-on experience building anomaly detection models, ideally within manufacturing or industrial environments.
• Strong experience working with high-frequency time series data, including feature engineering for sequential signals.
• Proficiency in Python and SQL, with experience using ML libraries such as TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of machine learning, statistical modeling, and data analysis techniques.
• Proven ability to work with large-scale datasets under low-latency constraints in production or near-production environments.
• Experience translating models into production systems (e.g., basic ML deployment, experiment tracking, or CI/CD workflows) is a plus.
• Strong communication skills in English (B2–C2), with the ability to clearly explain complex technical concepts and collaborate across teams.

We are seeking a strategic Analytics Manager to own and evolve our Center of Excellence (CoE). You will design and deliver scalable operational frameworks, spanning complex data project management and high-impact talent development.

The role combines process optimization, technical mentorship, and delivery assurance to drive operational excellence and superior technical output across Factored.

You will operate with an owner mentality, proactively identifying execution gaps, designing technical solutions, measuring performance metrics, and iterating on delivery processes. You will also collaborate closely with cross-functional teams to ensure alignment with client expectations and the high "Factored Bar" for quality.

Functional Responsibilities:

• Own core CoE processes, including recruitment, placement, onboarding, and structured career growth initiatives for the engineering team.
• Proactively design and implement scalable process improvements to increase organizational efficiency and drive innovation.
• Coach and mentor Engineering Leads and engineers, providing feedback to elevate technical seniority, performance, and professional ownership.
• Bridge strategy and execution, ensuring high-level initiatives are fully implemented, tracked, and delivered successfully.
• Ensure high-quality delivery across data projects, identifying risks early and resolving execution issues with clients and internal teams.
• Foster a culture of data rigor, maintaining the high "Factored Bar" by accurately measuring outcomes and technical deliverables.
• Collaborate cross-functionally to align resources, manage dependencies, and achieve shared organizational goals.
• Manage complex data projects from inception to completion, ensuring clear communication of results and stakeholder alignment.
• Drive accountability and follow-through across all initiatives, ensuring projects stay on track and meet defined success criteria.
• Monitor and optimize key CoE metrics, specifically focusing on resource utilization, placement success, and overall team performance.

Qualifications:

• 7+ years of total professional experience, with a consistent track record of professional growth and increasing responsibility.
• 5+ years of experience in specialized data roles, such as Data Analytics, Analytics Engineering, or Data Science, with deep domain expertise.
• 2+ years of experience in leadership roles, demonstrating the ability to manage high-performing teams or lead complex, multi-stakeholder projects.
• Advanced English proficiency, with exceptional written and verbal communication skills tailored for both technical and executive audiences.
• Mastery of data fundamentals, including advanced SQL, data modeling, ETL/ELT pipeline design, and end-to-end analytics workflows.
• Proven track record of execution, with a demonstrated ability to deliver high-impact results in fast-paced or ambiguous environments.
• Strong project management expertise, including proficiency in agile methodologies, resource planning, and risk mitigation.
• Experience in mentoring and coaching, with a focus on developing technical talent and fostering a culture of continuous learning.
• Strategic problem-solving skills, with a high degree of rigor in measuring outcomes and ensuring data integrity.
• Exceptional stakeholder management skills, acting as a bridge between technical execution and business strategy.

Nice to Have

• Experience in consulting or client-facing environments, with a proven ability to build technical proposals and manage high-stakes client relationships.
• Experience designing and implementing training programs or internal enablement curricula focused on the modern data stack (e.g., dbt, Snowflake, Databricks).
• Direct exposure to AI and Generative AI use cases, specifically in how they integrate with and enhance traditional analytics workflows.
• Proven success in scaling teams or operational processes within a rapidly growing startup or a center of excellence (CoE).
• Familiarity with advanced data governance and quality frameworks to support large-scale enterprise environments.
• Active participation in the data community, such as contributing to open-source projects, speaking at conferences, or technical blogging.

#Li-Remote

Our Program:

The Factored AI Machine Learning & GenAI Residency is a full-time, paid professional residency designed for experienced engineers who want to operate at a higher level of responsibility, complexity, and impact. This residency is not a course or a traditional training program. It is a selective, execution-driven environment where engineers validate and elevate how they build and operate real Machine Learning and Generative AI systems in production.

Residents work within real constraints, alongside peers who meet the same engineering bar, on systems that matter.

Highlights

• A full-time, paid residency with clear professional expectations
• Work on real production systems, not academic exercises or toy projects
• Ownership of system-level decisions across architecture, performance, reliability, and cost
• Continuous technical evaluation through real execution, not exams
• Exposure to the standards used to build and operate AI systems for U.S. companies

This residency is designed to function as a professional validation layer for senior engineers.

Structure & Support

The residency operates as a structured professional track focused on execution.

• Residency Period: 12 to 16 weeks, depending on readiness and scope
• Time Commitment: Full-time (approximately 45 hours per week)
• Environment: Hands-on execution, technical reviews, system-level discussions
• Compensation: Paid, reflecting the professional nature of the residency

Residents are supported with access to infrastructure, peer feedback, and senior technical guidance aligned with Factored’s engineering standards.

Qualifications

We are looking for engineers who already have strong foundations and real production experience.

Required background:

• 4+ years of experience building and deploying Machine Learning systems in production
• Strong Python proficiency, writing production-grade code
• Solid understanding of Deep Learning fundamentals, including model architecture, optimization, and training dynamics
• Experience working with NLP and modern ML frameworks such as PyTorch, TensorFlow, or Hugging Face
• Backend experience building APIs with FastAPI or Flask
• Experience with containerization and cloud deployment (AWS, Azure, or GCP)
• Familiarity with production MLOps practices, including CI/CD, model versioning, monitoring, and infrastructure as code
• Strong written and spoken English, with the ability to communicate technical decisions clearly
• Must be based in LATAM

This residency is not designed for beginners or engineers without prior production exposure.

Program Logistics

• Start Date: Ongoing for the first quarter of 2026
• Program Type: Full-time, paid residency
• Focus: Execution-first, production-grade AI systems
• Duration: 12 to 16 weeks
  
  

Factored does not charge upfront fees to participate. Any contractual terms related to participation and long-term engagement are communicated transparently during the process.

Admission Process

The admission process is designed to evaluate real-world readiness and system-level thinking.

• Online Assessment: Evaluation of core technical foundations and problem-solving ability
• Talent Interview: Assessment of communication, clarity of thought, and professional alignment
• Technical Interview: Deep dive into prior production experience and decision-making
• System Design Interview: Evaluation of architecture thinking, trade-offs, and cultural fit
  
  

Admission is selective by design.

We are seeking a Machine Learning Engineer who is passionate about building state-of-the-art recommender systems and leveraging Generative AI. You'll work with large-scale data using tools like Databricks and Spark, contributing to innovative AI solutions that enhance personalized experiences while being part of a supportive, dynamic, and collaborative team.  In return, you will be rewarded with an amazing team that supports you, a rich culture, shared success, and the flexibility to work– from the comfort of your home.

Functional Responsibilities:

• Design and implement recommender systems to improve product discovery and enhance customer engagement across digital and physical platforms.
• Build and manage scalable machine learning pipelines for data processing, feature engineering, model training, and deployment using tools like Databricks and Spark.
• Apply and optimize advanced machine learning models for recommendation systems, including Wide & Deep models, Two-Tower architectures, Transformer-based models (e.g., NRMS), embeddings-based approaches, neural networks, autoencoder-based models (e.g., AutoRec), and deep sequential models like GRU4Rec.
• Collaborate closely with software engineers, data scientists, and business stakeholders to integrate models into production systems and solve real-world business challenges.
• Monitor, maintain, and continuously enhance deployed models to ensure reliability, accuracy, and alignment with evolving business needs.
• Stay informed on the latest advancements in machine learning, recommender systems, deep learning, and Generative AI to drive innovation and improvement.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• 5+ years of proven experience as a Machine Learning Engineer, demonstrating successful development and deployment of Machine Learning models. 
• Minimum 1 year of hands-on experience designing, building, and deploying recommender systems. This is a must-have requirement.
• Strong programming skills in languages such as Python along with experience with machine learning libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.S
• olid understanding and application of machine learning techniques relevant to recommendation systems, including but not limited to Wide & Deep models, Two-Tower models, Transformers, embeddings, neural networks, autoencoders (AutoRec), and deep sequential models (GRU4Rec)
• Extensive experience handling large-scale data processing and analysis using Spark/PySpark within Databricks, including its native platform services.
• Solid understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
• Strong knowledge of experimental design, A/B testing, and performance evaluation metrics for machine learning solutions.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (Docker) is a plus.
• Excellent verbal and written communication skills in English.

We are seeking a skilled Machine Learning Engineer to join our team, with a specialized focus on Retrieval-Augmented Generation (RAG) models. The ideal candidate will have experience in designing, developing, and deploying advanced machine learning models that integrate retrieval and generation capabilities to create powerful AI-driven applications.

Functional Responsibilities:

• Design, develop, and optimize Retrieval-Augmented Generation (RAG) models that integrate retrieval-based and generation-based approaches to solve complex, real-world problems for our high-profile clients.
• Improve the performance of RAG models through cutting-edge algorithms, innovative techniques, and model fine-tuning.
• Collaborate with client Data and Engineering teams to establish and build robust machine learning infrastructure to meet project goals.
• Work closely with leadership teams from our clients to identify and leverage AI/ML opportunities that can provide transformative solutions.
• Fine-tune and adapt large language models (LLMs) for specific tasks and domains within the RAG framework.
• Partner with cross-functional client teams to deploy RAG models into production environments, ensuring seamless integration and long-term success.
• Apply advanced machine learning techniques, including LLMs, to develop effective AI solutions tailored to client needs.
• Write clean, maintainable, and scalable code, ensuring all development is well-documented and testable.
• Prioritize user experience and customer needs in all product development efforts.
• Design and develop frameworks for GenAI products, such as search interfaces, chatbots, and summarization tools.
• Build and implement machine learning models and algorithms that directly contribute to client growth and success through innovative, AI-driven solutions.
• Provide technical leadership in identifying and evaluating AI/ML opportunities that empower clients to deliver exceptional solutions.

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 5+ years of hands-on experience developing and deploying machine learning models in production environments.
• 4+ years of experience with production NLP and deep learning models using frameworks like PyTorch and TensorFlow.
• At least 1+ year of experience with Retrieval-Augmented Generation (RAG) and other advanced techniques to optimize model performance.
• Proven experience writing production-level code, with strong proficiency in Python.
• Expertise in working with large language models (LLMs) such as GPT, Gemini, and Claude, along with proficiency in LLM frameworks like LangChain.
• Strong understanding of prompting techniques, and the trade-offs between prompting and fine-tuning.
• Experience with cloud platforms such as AWS or GCP (AWS preferred), or equivalent on-premise platforms.

We are looking for a MLOps Engineer to join our team. You will participate in the development and maintenance of AI products for our clients. At Factored we are building a company that we all hold as our own, every single one of us. We need your skills to help take this rocketship to new heights and help create new opportunities for us.  In return, you will be rewarded with an amazing team that supports you, rich culture, shared success and the flexibility to work– from the comfort of your home.

Functional Responsibilities:

• Deploy machine learning models to production environments using industry best practices.
• Design and implement automated workflows for model training, testing, and deployment.
• Manage and optimize the infrastructure supporting machine learning workloads.
• Establish monitoring systems for deployed models to ensure performance and reliability.
• Work closely with data scientists, software engineers, and cross-functional teams to understand model requirements and business objectives.
• Ensure compliance with industry regulations and data protection standards.
• Stay informed about the latest advancements in MLOps and integrate relevant technologies and methodologies.
• Continuously optimize processes to enhance efficiency and reduce deployment times.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or a related field.
• Proven experience as MLOps Engineer, demonstrating successful development and maintenance of machine learning models.
• Strong programming skills in languages such as Python, R, or Java, along with experience with machine learning libraries/frameworks like TensorFlow, PyTorch, or scikit-learn.
• Solid understanding of machine learning algorithms, deep learning, and statistical modeling techniques.
• Experience with cloud platforms (e.g., AWS, Azure, GCP), Kubernetes, and Docker.
• Excellent verbal and written communication skills in English.

Functional Responsibilities:

• Architect, design, and implement backend systems that integrate with LLMs, and ensure services are scalable, secure, and production-ready
• Implement agentic architectures for complex AI workflows.
• Build, deploy, and manage cloud-native AI applications on AWS (Lambda, ECS, SageMaker) and other modern infrastructure.
• Fine-tune prompts and build RAG pipelines to optimize performance.
• Implement AI security guardrails and conduct risk assessments to ensure compliance, privacy, and safety.
• Monitor application health with observability tools (APM, logging, LLM performance metrics) and proactively resolve performance issues.
• Work closely with ML Engineers to consume and integrate trained models into production systems.
• Maintain high engineering standards: modular code, automated testing, CI/CD pipelines, and documentation.
• Partner with product managers, designers, and cross-functional teams to deliver user-facing AI features that drive measurable value.

Qualifications:

• 5+ years of professional experience as a Software Engineer or in a related role, with a strong foundation in Python.
• Hands-on experience or strong interest in Generative AI frameworks such as LangGraph, LangChain, OpenAI, and RAG implementations.
• Experience with cloud platforms like AWS, including building, deploying, and managing cloud-native applications; exposure to Azure or GCP is also valuable.
• Experience designing and developing APIs using frameworks like FastAPI, Django, or Flask; willingness to learn best practices for scalable production systems is appreciated.
• Strong system design and problem-solving skills, including building, testing, and optimizing backend systems or AI workflows.
• Practical experience with databases (PostgreSQL or NoSQL), and vector databases for RAG workflows.
• Proficiency with DevOps and CI/CD tools such as Docker, Kubernetes, Terraform, and Git-based workflows.
• Understanding of deep learning model development and deployment, with familiarity using frameworks like PyTorch or HuggingFace; motivated learners are welcome.
• Excellent English communication skills, both written and spoken, with the ability to collaborate effectively with global teams and explain complex AI concepts clearly.
• A growth mindset and genuine interest in learning and applying new AI/ML techniques to real-world challenges.

We are currently looking for a talented Analytics Engineer to join our team. You will play a critical role in designing, developing, and maintaining the data infrastructure and analytics solutions that support business operations.

Functional Responsibilities:

• Design, develop, and maintain data pipelines, data models, and ETL processes to support data integration, transformation, and aggregation.
• Work closely with stakeholders to understand their data requirements and translate them into scalable analytics solutions.
• Ensure data quality, accuracy, and consistency by implementing data validation, cleansing, and monitoring techniques.
• Identify and implement optimizations to improve the efficiency and performance of data pipelines and analytics processes.
• Build and test code using CI/CD best practices.
• Perform transformations using state-of-the-art data to create data models for consumption, often leveraging data stack tools like DBT.  
• Write and maintain data catalogs and definitions.
• Collaborate with data scientists and analysts to implement data-driven models, algorithms, and visualizations.

Qualifications:

• +5 years of experience working in data analytics or analytics engineering-related fields.
• Experience with the design of ETLs.
• Strong proficiency in programming languages such as Python, SQL, and T-SQL.
• Strong experience with Azure Data Factory, Synapse Analytics, or Snowflake.
• Experience with Data Visualization tools such as PowerBI.
• Previous experience performing advanced, modular SQL-based transformations.
• Experience applying software engineering best practices to analytics code (Version control, testing, continuous integration).
• Strong experience building excellent data documentation in company-wide data discovery tools.
• Excellent verbal and written communication skills in English.