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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Developer, Analytics & Insights - **Company:** BINGHAMTOM UNIVERSITY - **Location:** United States - **Experience:** Expert - **Salary:** $107,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Big Data, Information Engineering, Extract Transform Load (ETL), Apache Hadoop, Apache Hive, Python (Programming Language), SQL Databases, Data Streaming, Snowflake, Apache Spark, Amazon Virtual Private Cloud (VPC), Git, Information Technology, Machine Learning Operations, Presto, Restful APIs, Terraform, Autodesk Autocad, Jenkins - **Published:** September 23, 2026 - **Apply:** https://www.themuse.com/jobs/autodesk/senior-data-developer-analytics-insights?utm_source=uconnect ## About the Role * 5+ years of data processing and data engineering experience in a fast-paced, large cloud-based infrastructure (AWS experience required) * Hands-on software development experience in Python * Expert understanding of SQL, dimensional modeling, and analytical data warehouses, such as Snowflake, Presto/Hive * Understanding of Data Engineering best practices for medium to large scale production workloads * Knowledge of big data processing frameworks (e.g. Spark, Hadoop) * Expertise with data pipeline orchestration tools, such as Airflow * Familiar with processing semi-structured file formats such as Json or parquet * Team player with good communication skills * Problem solver with excellent written and interpersonal skills * Bachelor's degree in computer science, data science, or related fields, * Experience with Jinja, Shell scripting, DBT, Spark SQL * Experience developing in Cloud platform using serverless technologies such as AWS glue, lambda, EMR and EKS is a plus * Experience with remote development using AWS SDK is a plus * Experience with both ETL and ELT pipelines, including traditional ETL tools (e.g., Airflow, Talend, Informatica) and modern ELT frameworks (e.g., dbt, Snowflake) * Knowledge of AWS IAM roles, permissions, and best practices for least-privilege access * Experience with Terraform for AWS resource provisioning, including remote state management and security best practices * Hands-on experience with AWS networking (VPC, security groups, cross-account permissions) * REST API design and implementation * Familiarity with containers and infrastructure-as-code principles * Experience with automation frameworks - Git, Jenkins, and Terraform * Master's degree in computer science, data science, or related fields * Experience with AI-native data engineering technologies, including adoption of MCP (Model, Context, Prompt) related context engineering to optimize data flow for AI-driven tasks * Demonstrated ability to structure data for enhanced AI reasoning, enabling more robust and explainable machine learning workflows * Experience in reducing latency for complex agentic workflows, ensuring efficient real-time processing and responsiveness in AI-powered systems, * Expérience avec Jinja, les scripts Shell, DBT et Spark SQL * Une expérience en développement sur une plateforme cloud utilisant des technologies sans serveur telles qu'AWS Glue, Lambda, EMR et EKS est un plus * Une expérience en développement à distance à l'aide du SDK AWS est un plus * Expérience avec les pipelines ETL et ELT, y compris les outils ETL traditionnels (par exemple, Airflow, Talend, Informatica) et les frameworks ELT modernes (par exemple, dbt, Snowflake) * Connaissance des rôles IAM AWS, des autorisations et des meilleures pratiques en matière d'accès avec le principe du privilège minimal * Expérience avec Terraform pour l'approvisionnement en ressources AWS, y compris la gestion d'état à distance et les meilleures pratiques en matière de sécurité * Expérience pratique des réseaux AWS (VPC, groupes de sécurité, autorisations inter-comptes) * Conception et mise en œuvre d'API REST * Connaissance des conteneurs et des principes de l'infrastructure en tant que code * Expérience avec les frameworks d'automatisation : Git, Jenkins et Terraform * Master en informatique, science des données ou dans un domaine connexe * Expérience des technologies d'ingénierie des données natives pour l'IA, y compris l'adoption de l'ingénierie contextuelle liée au MCP (Modèle, Contexte, Invite) pour optimiser le flux de données pour les tâches pilotées par l'IA * Capacité avérée à structurer les données pour améliorer le raisonnement de l'IA, permettant des workflows d'apprentissage automatique plus robustes et explicables * Expérience dans la réduction de la latence pour des workflows agentiques complexes, garantissant un traitement en temps réel efficace et une réactivité optimale dans les systèmes alimentés par l'IA ## Description Autodesk is looking for a talented and motivated Senior Data Developer to join our Platform Strategy & Emerging Technologies (PSET) organization to develop robust and scalable data pipelines using and improving existing platforms, to support data-driven decision-making across the platform initiatives. The successful candidate will drive performance enhancements, build pipelines, and collaborate with analysts, data scientists, stakeholders and other Data Engineering teams across Autodesk. You will work with cutting edge technologies in the big data space., * Design, develop, automate and maintain scalable, robust and reliable ELT/ETL data pipelines that collect, process and transform large volumes of structure and unstructured data from various sources * Maintain and enhance our existing data architecture to ensure smooth and efficient data flow across platforms * Interface with data peers, product managers and cross-functional stakeholders to gather requirements, sequence work, and document technical solutions * Implement best practices for data quality, integrity and governance, including monitoring, validation and auditing processes to ensure reliable and consistent data availability * Contribute to a team culture that values quality, robustness, and scalability while fostering initiatives and innovation by staying up to date with industry trends and new technologies * Apply an AI-native data engineering mindset by using ML-driven validation, anomaly detection, automated schema evolution, automated data lineage and optimizations for pipeline performance and resource efficiency, * Concevoir, développer, automatiser et maintenir des pipelines de données ELT/ETL évolutifs, robustes et fiables qui collectent, traitent et transforment de grands volumes de données structurées et non structurées provenant de diverses sources * Maintenir et améliorer notre architecture de données existante afin d'assurer un flux de données fluide et efficace entre les plateformes * Collaborer avec les collègues chargés des données, les chefs de produit et les parties prenantes interfonctionnelles pour recueillir les exigences, organiser le travail et documenter les solutions techniques * Mettre en œuvre les meilleures pratiques en matière de qualité, d'intégrité et de gouvernance des données, y compris les processus de surveillance, de validation et d'audit afin de garantir une disponibilité fiable et cohérente des données * Contribuer à une culture d'équipe qui valorise la qualité, la robustesse et l'évolutivité tout en encourageant les initiatives et l'innovation en se tenant informé des tendances du secteur et des nouvelles technologies * Adopter une approche d'ingénierie des données native à l'IA en utilisant la validation basée sur le ML, la détection des anomalies, l'évolution automatisée des schémas, la traçabilité automatisée des données et les optimisations pour améliorer les performances des pipelines et l'efficacité des ressources ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [The Road to MLOps: How Verivox Transitioned to AWS](https://www.wearedevelopers.com/videos/1050-the-road-to-mlops-how-verivox-transitioned-to-aws) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Our GitOps approach for deploying an Identity Provider and an API Gateway in a SaaS company](https://www.wearedevelopers.com/videos/776-our-gitops-approach-for-deploying-an-identity-provider-and-an-api-gateway-in-a-saas-company) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)