Lead Data Engineer

United States Digital Space LLC; Mumbai; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

United States Digital Space LLC is seeking a senior Data Engineer in Mumbai to design and operate scalable data pipelines. You will work across Data Platform, Analytics, and ML teams to deliver reliable data products that power analytics and AI features.

The role emphasizes building with Python, SQL, and modern data stack tools. You will contribute to pipeline reliability, feature stores, and data governance while mentoring peers in a fast-moving fintech environment.

Qualifications

• 8+ years of professional software engineering with 4-5 years in data engineering.
• Hands-on experience with Modern Data Stack: ingestion, warehouse, transformation, orchestration, reverse ETL.
• Strong Python programming with clean, testable code and solid error handling.
• Fluent SQL and experience building data warehouses (BigQuery, Snowflake, Redshift).

Responsibilities

• Design, build, and maintain resilient data pipelines ingesting data from various sources into BigQuery.
• Write Python code using Declarative ELT frameworks to define testable pipelines.
• Build and operate ML feature pipelines with low latency data streams.
• Own the health of systems: monitoring, alerts, incident response.
• Collaborate with analytics engineers to validate schema and data quality.
• Partner with AI/ML platform team to design feature stores and model serving pipelines.
• Identify optimization opportunities to improve performance and velocity.
• Mentor junior engineers and contribute to technical decisions.

Skills

Python

SQL

Data engineering

BigQuery

Airflow

Tools

DLT

Fivetran

Airbyte

DBT

DBT/SQL

Airflow

Terraform

Cloud platforms

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 in6key locations around the world

We're a thriving community of over290innovative minds

We're a vibrant melting pot, celebrating over27nationalities in our team

Our team brings experience from over740previous companies, from startups to global giants

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

We’re proud to be an accreditedReal Living Wage employer, 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:

• 8+ years of professional software engineering experience, with at least 4-5 years in 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

the company is an equal opportunities employer. We welcome applications from all candidates, including individuals with disabilities and provide reasonable adjustments as required.

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.