data engineer

machine learning engineer

ai engineer

data scientist

software engineer

ai data engineer

Goldman Sachs

<div>
 
 Job Description
 
 
 
 
 The Opportunity
 
 
 
 Join a team building the data foundations that support the firm&rsquo;s AI and analytics capabilities. This role sits within the engineering effort to develop a modern Lakehouse and AI data platform that enables reliable, well-governed and high-performing data use across the firm.
 
 
 At Goldman Sachs, engineering teams are positioned at the centre of the business, building scalable systems, solving complex technical problems and turning data into action. In data engineering roles, the emphasis is on designing, building and maintaining large-scale data platforms, delivering production pipelines, improving reliability and quality, and partnering closely with users of the platform.
 
 
 This is a delivery-focused role for engineers who want to build robust data assets in production, work with modern data technologies, and grow over time within the firm. You will contribute to the data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases , and may also help extend platform tooling where additional functionality is needed.
 
 
 
 Role Summary
 
 
 
 As a
 
 Data Engineer
 
 in the
 
 Lakehouse and AI Data Platform team,
 
 you will design, build, test and support data pipelines and curated datasets on the firm&rsquo;s modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose. Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated.
 
 
 The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of delivery approaches across a wider set of use cases.
 
 
 
 Key Responsibilities
 
 
 
 
 Pipeline Engineering
 
 
 
 <ul>
 <li>
 Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
 </li>
 <li>
 Refactor or modernise existing data flows where needed to improve reliability, performance and maintainability.
 </li>
 <li>
 Where needed, build reusable tooling to improve delivery, consistency and operational support.
 </li>
 <li>
 Ensure data pipelines are production-ready, well tested and operationally supportable.
 
 
 </li>
 </ul>
 
 Data Modelling and Curation
 
 
 
 <ul>
 <li>
 Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
 </li>
 <li>
 Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
 </li>
 <li>
 Work with consumers to shape data products that are usable, well documented and aligned to business needs.
 
 
 </li>
 </ul>
 
 Data Quality and Reconciliation
 
 
 
 <ul>
 <li>
 Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
 </li>
 <li>
 Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
 </li>
 <li>
 Contribute to clear standards for testing, monitoring and issue resolution.
 </li>
 <li>
 Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.
 
 
 </li>
 </ul>
 
 Delivery and Partnership
 
 
 
 <ul>
 <li>
 Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
 </li>
 <li>
 Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.
 </li>
 <li>
 For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.
 
 
 </li>
 </ul>
 
 Required
 
 
 
 
 Skills and Experience
 
 
 
 <ul>
 <li>
 Bachelor&rsquo;s or master&rsquo;s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
 </li>
 <li>
 Strong hands-on programming experience in Python or Java.
 </li>
 <li>
 Good working knowledge of SQL, including troubleshooting, optimisation and data analysis.
 </li>
 <li>
 Ability to learn new tools, internal platforms and delivery workflows quickly.
 </li>
 <li>
 Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.
 
 
 </li>
 </ul>
 
 Data Engineering Capability
 
 
 
 <ul>
 <li>
 Understanding of temporal data modelling, including the handling of historical state and change over time.
 </li>
 <li>
 Knowledge of schema design, schema evolution and data compatibility considerations.
 </li>
 <li>
 Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
 </li>
 <li>
 Ability to make sensible design choices across normalised and denormalised models, and between natural and surrogate keys.
 </li>
 <li>
 Practical approach to data quality, reconciliation and root-cause analysis.
 </li>
 <li>
 Experience building or supporting production data pipelines in a collaborative engineering environment.
 </li>
 <li>
 Experience working with distributed data processing frameworks such as Apache Spark.
 </li>
 <li>
 Working knowledge of common data formats such as JSON, Avro and Parquet.
 
 
 </li>
 </ul>
 
 For More Experienced Candidates
 
 
 
 <ul>
 <li>
 Stronger ownership of technical design across multiple datasets or pipeline domains.
 </li>
 <li>
 Experience guiding implementation standards, code quality and engineering practices within a team.
 </li>
 <li>
 Ability to lead delivery for a workstream, manage dependencies and support less experienced engineers.
 
 
 </li>
 </ul>
 
 Technology Environment
 
 
 
 The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.
 
 
 
 Examples Of Technologies In Scope Include
 
 
 
 <ul>
 <li>
 Data processing and logic: ANSI SQL, Apache Spark, Kafka
 </li>
 <li>
 Data formats: JSON, Avro, Parquet
 </li>
 <li>
 Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
 </li>
 <li>
 Engineering and deployment: CI/CD tooling, containerised or Kubernetes-based deployment approaches where relevant
 
 
 </li>
 </ul>
 You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.
 
 
 
 What We Are Looking For
 
 
 
 We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes. It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective.
 
 
 
 Stronger Candidates Will Typically Demonstrate
 
 
 
 <ul>
 <li>
 sound judgement in technical trade-offs
 </li>
 <li>
 attention to detail in data correctness and testing
 </li>
 <li>
 a clear and structured approach to problem solving
 </li>
 <li>
 willingness to work closely with stakeholders and partner teams
 </li>
 <li>
 an ability to identify when delivery problems would be better solved through reusable tooling or platform improvements
 </li>
 <li>
 an interest in developing long-term expertise within the firm
 
 
 </li>
 </ul>
 
 About Goldman Sachs
 
 
 
 At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.
 
 
 We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.
 
 
 We&rsquo;re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html
 
 
 &copy; The Goldman Sachs Group, Inc., 2026. All rights reserved.
 
 
 Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.
</div>

Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Analyst/Associate - London

HeadFirst Group

<div>Build the global data backbone for AI and workforce intelligence as a Data Engineer at HeadFirst Group
Do you want to build scalable data platforms that power AI, analytics and intelligent workforce solutions on a global scale? Are you excited by modern Databricks environments, cloud-native engineering and the opportunity to help shape the future of AI-enabled platforms?
As a Data Engineer within the Global Data &amp; AI Team of HeadFirst Group &amp; Impellam Group, you will play a key role in building and scaling the data backbone behind our international Workforce-as-a-Service (WaaS) strategy. You will work on our internally developed Headless Data Architecture (HDA): a scalable, API-first lakehouse platform built on Azure Databricks, Delta Lake and Unity Catalog. This platform supports analytics, AI workloads and intelligent workforce solutions across Europe, North America and APAC.
What makes this role unique? You are not joining a traditional data team focused only on reporting or ETL. You will help build a modern global data platform where data engineering, AI enablement and platform scalability come together. You will work with modern technologies, AI-assisted development tooling and an international engineering team that is actively shaping the next generation of workforce intelligence.
Your impact at HeadFirst Group
As a Data Engineer, you are responsible for designing, building and optimising scalable and production-ready data pipelines within our global lakehouse ecosystem. You help create scalable and reusable data products powering analytics, AI and operational intelligence. You will contribute directly to the further development of our Headless Data Architecture (HDA), where governance and scalability are embedded into every layer of the platform.
In this role, you collaborate closely with ML/AI Engineers, Platform Engineers, Cloud Engineers and stakeholders across Europe, the UK, the US and APAC. Together, you help shape a modern cloud data platform that enables teams to exchange, govern and leverage data efficiently. You will join a growing international engineering team with significant ownership over platform decisions, engineering standards and the future direction of the platform.
What will you do?
Design, build and maintain Bronze Silver<ul><li> Gold medallion pipelines in Azure Databricks;</li><li>
Develop scalable ETL/ELT pipelines using PySpark, Delta Lake and Databricks-native tooling;</li><li>
Build deployable data products using Lakeflow, Spark Declarative Pipelines and Databricks Asset Bundles (DABs);</li><li>
Implement reusable data contracts and scalable data product patterns;</li><li>
Manage Unity Catalog governance, lineage and access management;</li><li>
Contribute to vector search, semantic search and AI-enabled retrieval capabilities;</li><li>
Work within a global multi-lakehouse architecture, including Delta Sharing across regions;</li><li>
Collaborate with international engineering teams on platform scalability and data exchange;</li><li>
Participate in Git-based development, CI/CD workflows and code reviews;</li><li>
Monitor platform health, observability and DataOps reliability;</li><li>
Work with AI-assisted development tooling such as Claude Code and the Databricks AI Dev Kit.</li></ul>
About HeadFirst Group
HeadFirst Group is a leading international workforce solutions provider and the largest Managed Service Provider in the Netherlands. Every day, we connect thousands of professionals with organisations such as Rabobank, Vattenfall, government institutions and many other public and private sector clients.
Through technology, data-driven insights and specialised expertise, we help organisations navigate an increasingly complex and rapidly changing labour market. Within our Global Data &amp; AI Team, we are building the foundation for a truly AI-enabled and data-driven organisation. You will join an ambitious international engineering environment where innovation, ownership and collaboration are central. We combine enterprise scale with modern engineering practices and continuously invest in cloud-native technologies, AI capabilities and platform maturity.
This is an opportunity to work on a modern Databricks ecosystem with real international impact, while helping shape the future of data and AI within a global organisation. 
We believe talent cannot always be captured in a checklist. Are you excited about the role, but do not meet every single requirement? We would still love to hear from you. Our recruitment process leaves room for different backgrounds, experiences and ways of working. Feel free to let us know what you need to perform at your best.<ul><li>
3+ years of hands-on Databricks experience in production environments;</li><li>
Strong experience with Medallion Architecture, Apache Spark, PySpark and Delta Lake;</li><li>
Experience with Databricks SQL, Unity Catalog and large-scale data engineering workloads;</li><li>
Familiarity with Lakeflow, Spark Declarative Pipelines and Databricks Asset Bundles (DABs);</li><li>
Strong SQL and Python skills;</li><li>
Experience with Git workflows, CI/CD and DataOps practices;</li><li>
Understanding of data contracts and scalable data product design;</li><li>
Familiarity with vector search, semantic search or AI-oriented data workloads is a plus;</li><li>
Experience in global or multi-lakehouse Databricks environments is a strong plus;</li><li>
Familiarity with Azure Synapse Analytics and SnapLogic;</li><li>
Strong communication skills and a pragmatic engineering mindset;</li><li>
Curiosity about AI-native engineering and modern platform development.</li></ul>
Ready to help shape the future of AI-driven workforce intelligence?
Apply today and become part of the Global Data &amp; AI Team at HeadFirst Group &amp; Impellam Group. Together, we are building the global data platform powering the next generation of workforce solutions. 
Mobility budget or Lease car: No
Brand: HeadFirst Group</div>

Data Engineer

Macro Hive

<div>
 
 
 Data Engineer &mdash; Markets &amp; AI Data Platform
 
 
 
 
 
 
 
 Macro Hive
 
 (BGC Group) is a leading independent provider of global macro strategy. We combine best-in-class human and artificial intelligence to provide institutional investors with market-leading investment opportunities
 
 
 .Our experts leverage large language models, such as those that power chatbots like ChatGPT, to develop innovative, data-driven solutions to complex financial problems and to build state-of-the-art models in search of alpha signals across asset classes
 
 
 
 
 
 
 Mission:
 
 
 
 Build the data backbone for AI, quant models and client tools.
 
 
 
 
 
 
 Owns:
 
 
 <ul>
 <li>
 Market, economic, research, news, internal and Fenics/BGC data pipelines
 </li>
 <li>
 Bronze/silver/gold data architecture
 </li>
 <li>
 Data quality gates
 </li>
 <li>
 Instrument, ticker, country, client, source and theme metadata
 </li>
 <li>
 APIs/tables for agents, dashboards and quant models
 </li>
 <li>
 Data lineage, entitlements, permissions and monitoring
 </li>
 </ul>
 
 
 
 
 
 Must have:
 
 
 <ul>
 <li>
 Strong Python and SQL
 </li>
 <li>
 Production ETL/ELT experience
 </li>
 <li>
 Cloud data stack: Azure, Databricks, Delta Lake, Snowflake or similar
 </li>
 <li>
 Time-series or complex-data experience
 </li>
 <li>
 Data quality, monitoring and audit awareness
 </li>
 <li>
 Ability to turn messy data into clean model-ready datasets
 </li>
 </ul>
 
 
 
 
 
 Nice to have
 
 
 <ul>
 <li>
 Experience with ticker level data using for example kdb+
 </li>
 </ul>
 
 
 
</div>

Speechify

<div>The mission of Speechify is to make sure that reading is never a barrier to learning.

Over 50 million people use Speechify's text-to-speech products to turn whatever they're reading &ndash; PDFs, books, Google Docs, news articles, websites &ndash; into audio, so they can read faster, read more, and remember more. Speechify's text-to-speech reading products include its iOS app, Android App, Mac App, Chrome Extension, and Web App. Google recently named Speechify the Chrome Extension of the Year and Apple named Speechify its 2025 Design Award winner for Inclusivity.
Today, nearly 200 people around the globe work on Speechify in a 100% distributed setting &ndash; Speechify has no office. These include frontend and backend engineers, AI research scientists, and others from Amazon, Microsoft, and Google, leading PhD programs like Stanford, high growth startups like Stripe, Vercel, Bolt, and many founders of their own companies.

Overview

We're looking to hire for our Data side of our AI team at Speechify. This role is responsible for all aspects of data collection to support our model training operations. We are able to build high-quality datasets at petabyte-scale and low cost through a tight integration of infrastructure, engineering, and research work. We are looking for a skilled Software Engineer to join us.

What You'll Do
<ul>
<li>Be scrappy to find new sources of audio data and bring it into our ingestion pipeline</li>
<li>Operate and extend the cloud infrastructure for our ingestion pipeline, currently running on GCP and managed with Terraform.</li>
<li>Collaborate closely with our Scientists to shift the cost/throughput/quality frontier, delivering richer data at bigger scale and lower cost to power our next-generation models.</li>
<li>Collaborate with others on the AI Team and Speechify Leadership to craft the AI Team's dataset roadmap to power Speechify's next-generation consumer and enterprise products.</li>
</ul>
An Ideal Candidate Should Have
<ul>
<li>BS/MS/PhD in Computer Science or a related field.</li>
<li>5+ years of industry experience in software development.</li>
<li>Proficiency with bash/Python scripting in Linux environments</li>
<li>Proficiency in Docker and Infrastructure-as-Code concepts and professional experience with at least one major Cloud Provider (we use GCP)</li>
<li>Experience with web crawlers, large-scale data processing workflows is a plus</li>
<li>Ability to handle multiple tasks and adapt to changing priorities.</li>
<li>Strong communication skills, both written and verbal.</li>
</ul>
What we offer
<ul>
<li>A fast-growing environment where you can help shape the company and product.</li>
<li>An entrepreneurial-minded team that supports risk, intuition, and hustle.</li>
<li>A hands-off management approach so you can focus and do your best work.</li>
<li>An opportunity to make a big impact in a transformative industry.</li>
<li>Competitive salaries, a friendly and laid-back atmosphere, and a commitment to building a great asynchronous culture.</li>
<li>Opportunity to work on a life-changing product that millions of people use.</li>
<li>Build products that directly impact and support people with learning differences like dyslexia, ADD, low vision, concussions, autism, and more.</li>
<li>Work in one of the fastest-growing sectors of tech, the intersection of artificial intelligence and audio.</li>
</ul>
Think you're a good fit for this job?

Tell us more about yourself and why you're interested in the role when you apply.
 
And don't forget to include links to your portfolio and LinkedIn.

Not looking but know someone who would make a great fit?

Refer them!

Speechify is committed to a diverse and inclusive workplace.

Speechify does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.</div>

Software Engineer, Data Infrastructure & Acquisition - London, United Kingdom

Duvo Inc

About the role
 
We already collect a lot of data — now we need someone to turn it into leverage. This is not a classic pipelines-and-warehouse hire (that's table stakes). It's an AI-native take on the data role: build the foundations, then build the agents and internal workflows on top that make the whole organization faster and more data-driven.
 
What you'll own
 
The data foundation — pipelines, warehouse, the substrate. Necessary, but not the point.
 
Agents and internal workflows built on top of that foundation — the actual leverage.
 
Enabling the org (product and beyond) to make faster, data-driven decisions.
 
This is an AI-native, senior role for someone who builds. You'll work across the data substrate and the agentic layer on top of it, and you'll be measured by how much faster and smarter you make the rest of the team.
 
Compensation Range: €135K - €190K

Data Engineer (EU/UK Based - Remote)

duvo.ai

<div>
 
 About The Role
 
 
 
 We already collect a lot of data &mdash; now we need someone to turn it into leverage. This is not a classic pipelines-and-warehouse hire (that's table stakes). It's an AI-native take on the data role: build the foundations, then build the agents and internal workflows on top that make the whole organization faster and more data-driven.
 
 
 What you'll own
 
 
 The data foundation &mdash; pipelines, warehouse, the substrate. Necessary, but not the point.
 
 
 Agents and internal workflows built on top of that foundation &mdash; the actual leverage.
 
 
 Enabling the org (product and beyond) to make faster, data-driven decisions.
 
 
 This is an AI-native, senior role for someone who builds. You'll work across the data substrate and the agentic layer on top of it, and you'll be measured by how much faster and smarter you make the rest of the team.
 
 
 Compensation Range: &euro;135K - &euro;190K
 
 
</div>

Station

<div>
 
 
 Python Engineer
 
 
 
 
 
 
 
 (AI / LLM Focus)
 
 
 
 
 
 
 London (Hybrid) | 🚀 Fintech Scale-Up
 
 
 
 
 
 We&rsquo;re partnering with a fast-growing fintech company in London that&rsquo;s redefining how financial data is leveraged through cutting-edge AI. They&rsquo;re now looking for a Python Engineer with strong experience in AI and Large Language Models (LLMs) to help build intelligent, scalable products used by thousands of customers.
 
 
 
 
 
 This is a high-impact role where you&rsquo;ll work closely with product, data, and engineering teams to bring AI-driven features into production.
 
 
 
 
 
 
 What you&rsquo;ll be doing:
 
 
 <ul>
 <li>
 Designing and building scalable backend systems using Python
 </li>
 <li>
 Developing and integrating LLM-powered features into core products
 </li>
 <li>
 Working with modern AI frameworks (e.g. OpenAI, LangChain, Hugging Face)
 </li>
 <li>
 Collaborating with data scientists to productionise models
 </li>
 <li>
 Improving system performance, reliability, and scalability
 </li>
 <li>
 Contributing to architectural decisions in a growing engineering team
 </li>
 </ul>
 
 
 
 
 
 What they&rsquo;re looking for:
 
 
 <ul>
 <li>
 Strong experience with Python in a production environment
 </li>
 <li>
 Hands-on experience with LLMs or applied AI (prompt engineering, fine-tuning, RAG, etc.)
 </li>
 <li>
 Experience building APIs and backend services
 </li>
 <li>
 Solid understanding of cloud platforms
 </li>
 <li>
 Ability to work in a fast-paced, product-driven environment
 </li>
 </ul>
 
 
 
 
 
 Why join?
 
 
 <ul>
 <li>
 Work on genuinely impactful AI products in fintech
 </li>
 <li>
 Strong engineering culture with a focus on innovation
 </li>
 <li>
 Backed by top-tier investors
 </li>
 <li>
 Competitive salary + meaningful equity
 </li>
 <li>
 Flexible hybrid working in London
 </li>
 </ul>
 
 
</div>

Python Developer

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547 ai data engineer jobs In London

Software Engineer - Data, Lakehouse and AI Data Platform Engineer - Analyst/Associate - London

Data Engineer

Data Engineer

Software Engineer, Data Infrastructure & Acquisition - London, United Kingdom

Data Engineer (EU/UK Based - Remote)

Data Engineer (EU/UK Based - Remote)

Python Developer