Lead Data Engineer

liberis; Mumbai; On-site; INR 3,500,000 - 7,000,000; Full time

Benefits offered by this job

Career development

Hybrid work culture

Job summary

Liberis is building an embedded finance platform and is expanding its Data & Insights team in Mumbai and globally. We are seeking a Senior Data & Insights Engineer to design and operate scalable data pipelines, feature stores, and ML data feeds that power decisioning across products.

You will own end-to-end data infrastructure, collaborate with analytics engineers and the ML platform team, and help raise data quality, observability, and developer velocity in a fast-paced fintech environment.

Qualifications

• 8+ years of professional software engineering experience, including 4–5 years in data engineering.
• Hands-on experience with Modern Data Stack architectures: ingestion, warehouse, transformation, orchestration.
• Strong Python programming and fluent SQL.
• Experience with cloud data platforms like BigQuery, Redshift, Snowflake.
• Experience with IaC tools (Terraform, CloudFormation, Pulumi) and CI/CD.
• Ability to thrive in fast-moving environments with reliability focus.
• Understanding of DevOps principles: observability, incident response, and monitoring.

Responsibilities

• Design, build, and maintain resilient data pipelines ingesting data from Azure SQL, SaaS platforms, and event streams into BigQuery.
• Write Python code to define declarative, testable, version-controlled pipelines.
• Build and operate ML feature pipelines—low-latency, real-time features feeding ML models.
• Own the operational health of systems you build with monitoring, alerting, and incident response.
• Collaborate with analytics engineers to validate data needs, schema design, and data quality standards.
• Partner with the AI/ML platform team to design feature stores and model serving pipelines.
• Identify and execute optimisation work to improve performance, reliability, and developer velocity.
• Mentor junior engineers and participate in platform-direction decisions.
• Work cross-functionally with product teams, analytics engineers, BI, and ML teams to shape data requirements.

Skills

Python

SQL

Data pipelines

BigQuery

DBT

Airflow

Tools

DLT

Fivetran

Airbyte

BigQuery

Snowflake

Redshift

DBT

Airflow

Terraform

CloudFormation

Pulumi

Job description

Some key info for you about Liberis:

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

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

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

Our Product & Engineering Team:

Liberis 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 Liberis 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 Liberis' 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 Liberis, with progression opportunities for both individual contributors and people managers. You can have a look through our Engineering Career Framework via this link.

Liberis 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 Liberis culture.

Our hybrid working policy requires team members to be in the office at least 3 days a week. At Liberis, 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.

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