Lead Data Engineer

United States Digital Space LLC; Greater London; On-site; GBP 70,000 - 120,000; Full time

Job summary

United States Digital Space LLC is seeking a data engineer to help build and operate scalable data pipelines and analytics platforms. You’ll work on composable data platforms, ML feature pipelines, and real-time data streams across cloud services in a hybrid London setting.

You’ll collaborate with product, BI, and ML teams to ensure data quality, governance, and reliability while accelerating delivery and impact.

Qualifications

• Proven experience within data engineering roles -building and operating data pipelines at scale.
• Hands-on experience with Modern Data Stack architectures and related tools.
• Strong Python programming with clean, testable code and good error handling.
• Fluent SQL writing, understanding execution plans and performance optimization.
• Experience with cloud data platforms and IaC tools, and CI/CD deployments.
• Able to work in fast-moving environments and apply DevOps practices.

Responsibilities

• Design, build, and maintain resilient data pipelines ingesting data from diverse sources into data warehouses.
• Write Python code to define declarative, testable pipelines and manage feature pipelines for ML.
• Collaborate with analytics engineers to ensure data quality and schema consistency.
• Partner with AI/ML platforms to design feature stores and model serving pipelines.
• Identify optimization opportunities to improve reliability, performance, and velocity.
• Mentor junior engineers and contribute to technical decision making.

Skills

Data pipelines

Modern Data Stack

Python

SQL proficiency

BigQuery

DBT

Airflow

Cloud platforms

DevOps basics

Observability

Tools

DLT

Fivetran

Airbyte

BigQuery

Snowflake

Redshift

DBT

Airflow

Terraform

Job description

Some key info for you about the company:

We were founded in 2007

We have provided over $3bn of funding to small businesses so far

We have been named in CNBC & Statista Top 150 UK Fintechs for 2025

We’re a global team, with a dynamic presence in 6 key locations around the world

We’re a thriving community of over 290 innovative minds

We’re a vibrant melting pot, celebrating over 27 nationalities in our team

Our team brings experience from over 740 previous companies, from startups to global giants

We have just been named as one of FinTech’s Finest 50by Welcome to the Jungle

We’re proud to be an accredited Real Living Wageemployer, ensuring everyone is paid fairly for the great work they do!

Our Product & Engineering Team:

the company is building the embedded finance platform that lets partners around the world offer innovative funding products to their small business customers. We're a growth-stage fintech with teams in London, Nottingham, Atlanta, Stockholm, Munich and Mumbai, and we’re building a global Product, Data & Engineering team that thrives on autonomy, ownership, and is focused on impact! Our teams solve real-world problems for small businesses, shaping products that unlock opportunity at scale.

Engineering is going through an AI-first transformation, rethinking how teams are structured and how they ship. It's changing what a small team can do! We empower our teams to make decisions, move fast, and take full responsibility for the solutions they deliver. You’ll join a team where curiosity is encouraged and collaboration across Product, Data, Delivery and Engineering is the norm.

About our Data & Insights Team:

We exist to build the data platforms and analytics that enable every decision at the company to be data-informed—and increasingly, to power AI and ML capabilities across the company!

We're building composable, reliable data platforms that scale—from ingesting partner transaction data and event streams, to powering analytics dashboards, to feeding ML models with real-time features. We're also supporting the AI/ML platform team with reliable, low-latency feature pipelines and model serving infrastructure.

We're collaborative, pragmatic, and we value moving fast by fixing the right problems—not over-engineering, but building to last!

The team is made up of three functions:

Data Platform Engineering:

Building and scaling ELT pipelines, managing data infrastructure on GCP, and creating the foundation for analytics and ML feature stores. You'll be part of a small, high-performing team of platform engineers focused on reliability, scale, and developer velocity.

Analytics Engineering:

Transform raw data into trusted models using DBT and SQL, powering self-serve analytics and business intelligence for stakeholders across the company.

Data & Business Intelligence:

Build dashboards, partner-facing reports, and insights that drive business decisions and revenue outcomes.

What you'll get to do in the role:

• Design, build, and maintain resilient data pipelines that ingest data from Azure SQL, SaaS platforms, and event streams into BigQuery.
• Write Python code using DLT to define declarative, testable, version-controlled pipelines - no low-code tools, real engineering.
• Build and operate ML feature pipelines - low-latency, real-time data streams that feed ML models with accurate, fresh features.
• Own the operational health of systems you build - monitoring, alerting, error handling, and incident response. When the data pipeline goes down, merchant credit decisions and ML model predictions suffer.
• Collaborate with analytics engineers to understand data needs, validate schema design, and establish data quality standards that both analytics and ML rely on.
• Partner with the AI/ML platform team to design feature stores, streaming feature infrastructure, and model serving pipelines that power the company' decisioning engine.
• Identify and execute optimisation work - improving performance, reliability, and developer velocity without rearchitecting stable systems.
• Mentor junior engineers, helping them grow as engineers and supporting their career development.
• Participate in technical decisions about platform direction - infrastructure choices, tooling, architecture trade-offs.
• Work cross-functionally with product teams, analytics engineers, BI specialists, and the ML platform team to shape data requirements and platform capabilities.

What we think you'll need:

• Proven experience within data engineering roles -building and operating data pipelines at scale
• Hands-on experience building Modern Data Stack architectures - you understand the layers: ingestion, warehouse, transformation, orchestration, reverse ETL. You've worked with tools like DLT/Fivetran/Airbyte (ingestion), BigQuery/Snowflake/Redshift (warehouse), DBT (transformation), Airflow/similar (orchestration).
• Strong Python programming - you write clean, testable, maintainable code with solid error handling and logging.
• Fluent SQL - you can write complex queries, understand execution plans, and optimize for performance and cost.
• Experience with cloud data platforms - you've built data warehouses in BigQuery, Redshift, Snowflake, or similar; you understand distributed processing, partitioning, cost optimization, and data governance.
• Experience with infrastructure-as-code tools (Terraform, CloudFormation, Pulumi) or equivalent - you version control infrastructure and deploy it via CI/CD pipelines.
• Experience working in fast-moving environments where requirements evolve and you adapt quickly without losing sight of reliability.
• Understanding of DevOps principles - you think in terms of observability, resilience, incident response, and operational excellence. You can set up monitoring and alerting that actually matters.

Bonus points if you have:

• Experience with DLT or similar declarative ELT frameworks; experience with Google Cloud Platform ecosystem (BigQuery, Cloud Run, Pub/Sub, Dataflow); experience with Kafka, Pub/Sub, or event streaming platforms; experience scaling data systems from 0 to 100M+ events/day; experience implementing data quality frameworks (Great Expectations, dbt tests, custom monitoring); background in fintech or high-stakes data reliability environments where data quality directly impacts revenue.
• Experience working with distributed, asynchronous teams across timezones; experience in India tech ecosystem or building in resource-constrained environments; experience migrating from legacy data infrastructure (Azure ADF, traditional ETL) to modern cloud-native stacks.

Career development is really important to us here at the company, with progression opportunities for both individual contributors and people managers. You can have a look through our Engineering Career Framework via this link

Our hybrid approach

Working together in person helps us move faster, collaborate better, and build a great the company culture. Our hybrid working policy requires team members to be in the office at least 3 days a week. At the company, we embrace flexibility as a core part of our culture, while also valuing the importance of the time our teams spend together in the office.