Manager - Data Engineer

About MoR

MoR is an AI-native broking platform for serious Indian F&O traders. Traders interact through natural language - they ask questions, explore strategies, and place orders through conversation. The platform learns from every interaction to personalize what each trader sees and how it responds.

The Role

We are building a system that remembers every trader - what they search, what they trade, what they watch - and uses that to personalize their experience. Their home feed shows relevant cards (earnings alerts, unusual OI, strategy ideas). The AI chat adapts to their trading style. Proactive alerts fire when something matters to them specifically.

You will design and build the data architecture that makes all of this work. The event pipelines that capture trader behavior. The profile store that represents who each trader is. The feature layer that feeds ranking models and the AI system. The infrastructure that serves it all in real-time.

We need someone who has built this kind of system before - at a company like Flipkart, Swiggy, Instagram, etc. and can now bring that experience to a smaller, higher-stakes domain where data decisions have financial consequences.

What You Will Do

Build the event pipeline: Capture every trader interaction (chat queries, option chain views, orders, feed taps) into a clean, typed event stream that downstream systems can consume in real-time.

Design the trader profile: Create a persistent, evolving representation of each trader - their preferred stocks, strategy sophistication, trading patterns, sector interests - built from actual behavior, not questionnaires.

Power the feed: Build the data layer that lets the feed ranking system decide which cards to show each trader, in what order, mixing market events with personalized content.

Feed the AI: Inject trader context into the AI chat system so the platform's responses adapt - a Bank Nifty weekly trader gets different defaults than a monthly Nifty position holder.

Build the feature layer: Design and serve computed features (sector affinity scores, recency-weighted interests, strategy complexity level) to ranking models with low latency.

Handle cold start: New users should see a useful, personalized experience from day one - design the progressive profiling strategy that makes this work with sparse data.

Own data quality: Build monitoring for pipeline health, data freshness, and feature drift. In a financial platform, stale or wrong data isn't just a bad experience - it's dangerous.

What You Bring

Recommendation system experience: You have built the data infrastructure behind a production recommendation or personalization system. Not used one - built the pipelines, the feature computation, the serving layer underneath one.

Scale experience: You have worked at a company with millions of users and dealt with high-volume event streams, real-time feature computation, and production data systems that can't go down.

Event streaming: Hands-on with Kafka, NATS, Pulsar, or equivalent. You've designed event schemas, managed consumer groups, and handled the messy reality of exactly once processing.

Storage decisions: You've chosen between Postgres, Redis, Cassandra, ClickHouse, and vector databases based on actual access patterns - and you can explain why you picked what you picked.

Feature serving: Experience with feature stores or equivalent - computing features offline and serving them online with consistency, at low latency.

Python/Go: Strong coding skills for pipeline and tooling work.

Education

• Bachelor’s/master’s degree in computer science, Engineering, Data Science, or related field.

Required Skills & Experience

• 4+ years building production-grade data engineering systems

• Strong Python and SQL fundamentals

• Experience designing and maintaining ETL / ELT pipelines

• Experience with cloud-native data platforms (AWS preferred)

• Strong understanding of data modelling and warehouse design

• Experience operating relational and NoSQL databases in production

• Solid grasp of data security fundamentals (IAM, PAM, encryption, access control)

• Ability to reason about performance, cost, and reliability trade-offs

Good to Have

• Experience with financial, trading, or wealth management data

• Exposure to streaming or event-driven data systems

• Experience with data governance, lineage, or cataloging

• Familiarity with compliance-driven environments (SEBI, SOC, ISO)

• Experience working closely with InfoSec or risk teams

What We Value

• Engineers who think in systems, not scripts

• Strong ownership of data correctness and security

• Bias toward reliability over cleverness

• Comfort operating in regulated environments

• Engineers who debug production issues end-to-end