> Markdown version of [/jobs/ext/2190764-data-engineering-specialist-snowflake](https://www.wearedevelopers.com/jobs/ext/2190764-data-engineering-specialist-snowflake). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineering Specialist (Snowflake) - **Company:** Morgan Stanley - **Location:** Glasgow, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, ARM Architecture, Microsoft Azure, Cloud Computing, Code Review, Databases, Data as a Services, Data Architecture, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Data Warehousing, Apache Hadoop, Python (Programming Language), NoSQL, Queueing Systems, SQL Databases, Data Streaming, Unstructured Data, Jupyter Notebook, Snowflake, Apache Spark, Git, Data Analytics, Integration Frameworks, Apache Kafka, Software Version Control, Data Pipelines, Databricks - **Published:** August 23, 2026 - **Apply:** https://www.totaljobs.com/job/data-engineering-specialist/morgan-stanley-job107885836 ## About the Role * Data Engineering experience with Snowflake and Databricks native solutions. * Experience on Snowflake Cortex is must. * Proficiency in programming skills in Python. * Experience with data processing frameworks like Apache Spark or Hadoop. * Knowledge of database systems (SQL and NoSQL). * Familiarity with cloud platforms (AWS, Azure) and their data services. * Understanding of data modeling and data architecture principles. * Experience with data warehousing concepts and technologies. * Experience with message queues and streaming platforms (e.g., Kafka). * Experience with version control systems (e.g., Git). * Experience using Jupyter notebooks for data exploration, analysis, and visualization. * Excellent communication and collaboration skills. * Ability to work independently and as part of a geographically distributed team. * 8 years+ of being a practitioner in data engineering or a related field would generally be expected to find the skills required for this role. ## Description We are seeking a Data Engineering Specialist to join the Architecture & Modernization team. You will be instrumental in building and maintaining the data infrastructure for our Data AI platforms. This role will involve hands-on development, data pipeline creation, and close collaboration with stakeholders across the organization. This role requires a self-starter with strong execution skills and the ability to work independently. You will be expected to not only execute on the current strategy but also contribute to its evolution. We value diversity of thought and are committed to building a team that reflects the diversity of our global community. Our mission is to develop a firmwide Artificial Intelligence (AI) Development Platform that aligns with the firm's Technology principles and drives efficiency and consistency, controls, security and strong governance and promotes innovation, enabling teams to build applications that leverage AI capabilities and accelerate the adoption of AI across our businesses. In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Data & Analytics Engineering position at Director level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques. Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world. What you'll do in the role: * Develop and maintain data pipelines and ETL (Extract, Transform, Load) processes. * Work with structured and unstructured data to ensure it is accessible and usable. * Optimize data systems for performance and scalability. * Implement data quality and data governance standards. * Collaborate with stakeholders across technology and business units to understand their data needs and translate them into technical solutions and provide data-driven insights. * Contribute to the documentation and knowledge sharing within the team, creating, and maintaining technical documentation and training materials. * Participate in code reviews and contribute to the improvement of development processes. * Contribute to the broader data architecture community through knowledge sharing, presentations., If this role is deemed a Certified role and may require the role holder to hold mandatory regulatory qualifications or the minimum qualifications to meet internal company benchmarks. Flexible work statement Interested in flexible working opportunities? Morgan Stanley empowers employees to have greater freedom of choice through flexible working arrangements. Speak to our recruitment team to find out more. Morgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents. Our workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences. ## Related Videos - [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) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [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) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [Beyond SQL Generation: How to Teach Agents What Your Database Actually Means](https://www.wearedevelopers.com/videos/100127-beyond-sql-generation-how-to-teach-agents-what-your-database-actually-means) ## Related Articles - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [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)