Lead - Data Engineer

Founded by ex-Flipkart leader Aditya Soni and backed by 3one4 Capital, Venture Highway, Ram Shriram, Lloyd Mathias and more, CheQ isn’t just another fintech, it’s a full-stack credit experience

CheQ is at the point where data is no longer a side function supporting the business — it’s the foundation underneath every product line, every partnership, and every decision. We’re processing billions of records today across multiple databases, warehouses, event streams, and partner data flows, and the volume is growing fast.

We’re hiring a leader to design and build CheQ’s data platform for the next 3-5 years. This is both an architecture role and a hands-on engineering role: you’ll set the target-state design, decide where each class of data should live, define the business data layer that analytics and AI build on top of, establish governance and quality standards, and personally build the critical pieces in year one before scaling into a small team in year two. This is explicitly not "the first person who writes pipelines." It’s the person who defines CheQ’s data architecture and operating model — the pipelines are one of several things that follow from the architecture choices they make.You’ll report into the Head of Data, work closely with the CTO on architectural decisions, and partner with Engineering, Analytics, Product, and Lending.

What you’ll own

1. Data architecture & strategy

Define the target-state architecture for CheQ’s data platform. Decide how operational databases, warehouse, partner data, event streams, and future ML workloads should flow and where each class of data should live. Produce a 12-24 month roadmap that leadership can commit resources against — not just a backlog of pipelines.

2. Business data layer and modeling

Move CheQ from raw replicated data to trusted, reusable business datasets. Define canonical entities and metrics — customer, transaction, payment, merchant, settlement, and others — so that every team works from the same source of truth and analytics isn’t re-interpreting raw tables every time a question comes up.

3. Data quality, lineage, and governance

Establish ownership, freshness contracts, reconciliation, schema evolution, lineage, PII handling, and auditability as first-class engineering concerns. For a fintech, correctness and traceability matter as much as uptime — this isn’t a compliance checkbox, it’s part of the architecture.

4. Analytics and self-service enablement

The objective isn’t to land data in a warehouse — it’s to make trusted data easily consumable by analytics, BI, product, and eventually AI teams. Design the semantic/business layer, standardized metrics, and tooling patterns that make analysts productive without re-asking the data team every time.

5. AI/ML readiness

Design the platform so future AI/ML use cases don’t require a rebuild. Historical datasets must be accessible, feature-ready data available where needed, real-time pipelines viable where latency demands it, and experimentation supported — all considered up front, not retrofitted later.

6. Cost and scale economics

Own data platform economics end-to-end. Warehouse optimization is table stakes; the broader concern is cost per decision, cost per model refresh, cost per analytical workload. Design for current volume (~8 billion records) and materially larger volumes over the next few years.

7. Team and operating model

Build the data engineering function over the next 12-18 months. Establish engineering standards, ownership models, tooling principles, hiring plan, and the operating rhythm — not just hire more pipeline engineers. The team you build will outlast any specific technology choice; the standards and culture you set will be their multiplier.

What you’ll have delivered in 12 months

• A written and ratified target-state data architecture, with trade-offs documented and the 12-24 month roadmap sequenced against it

• A canonical business data layer in production, with at least 10 core entities modeled and consumed by downstream analytics

• A governance framework in operation — data ownership assignments, SLAs on freshness and quality for critical datasets, lineage visibility, PII handling policy

• A functioning self-service analytics layer — analysts and non-technical consumers productive without routing every question through data engineering

• Measurable improvements in data platform cost per unit workload, with a defensible economic model for how costs scale with growth

• A hired and ramped data engineering team of 2-3 people, operating within the standards and ways of working you’ve established

What we’re looking for

Must have skills

• 10+ years of experience across data engineering, data platform, and architecture roles, with time spent in a strategic/leading role as well as hands-on building.

• Demonstrated architectural judgment — you’ve designed data platforms end-to-end, made the "where should this data live, which engine, which pattern" calls at scale, and can explain the trade-offs behind every choice (not just defend the choice itself).

• Business data modeling experience — defining canonical entities, dimensional modeling, metrics layers. You’ve built the kind of data layer that makes a BI team productive instead of frustrated.

• Governance as design, not bolt-on — practical experience with data contracts, lineage tooling, ownership models, PII handling, and the engineering patterns that make these work at scale.

• Hands-on execution bar is unchanged by seniority — you still write code, still debug pipelines when they break, still make the architecture real. We’re hiring an architect who ships, not one who presents slides.

• Cost-sensitivity instinct — you have a mental playbook for auditing data platform spend, know where the typical waste sits in a warehouse-centric stack, and have driven real cost reductions before.

• Experience in a regulated industry (financial services, healthcare, or equivalent) — understands why audit trails, data residency, and correctness carry non-negotiable weight. 

• Prior experience as the data architecture lead at a Series B/C fintech or scaling consumer company — ideally the person who defined the "next 3 years" of the data function.

• Experience designing for AI/ML workloads at the platform layer — feature stores, real-time serving, experimentation infrastructure — even if you didn’t build the ML models yourself.

• Comfort moving across batch, streaming, and transactional patterns and choosing between them based on actual latency, reliability, and cost needs rather than preference.

• Experience building and leading a data engineering team through its first growth phase.

• Semantic layer / metrics layer experience — dbt Semantic Layer, Cube, LookML, or equivalent in-house implementations.

WHY SHOULD YOU JOIN CHEQ

Shape your work with freedom, deliver with ownership, backed by trust.

Bold, fast & human
We push boundaries together, and deliver with agility, rigor, and heart. Always looking out for one another.

Growth by osmosis
Learn directly from a stellar leadership team with rich, diverse backgrounds.

WHAT YOU WILL NOT GET

• Monotony → Work is dynamic, expect variety every day.
  

• Slow climbs → Growth here is about leaps, not ladders.
  

• Layers of red tape → We believe in speed, not bureaucracy.