Data Platform Engineer (Distributed Systems & Graph Analytics)

Job Description

We’re looking for a Data Platform Engineer to take end-to-end ownership of large-scale data pipelines and distributed systems powering a next-generation Graph Analytics platform.

What You’ll Own

Data Pipeline & Platform Engineering

Design and build end-to-end data pipelines (batch + streaming)

Own large-scale Apache Spark workloads and distributed data processing

Implement data ingestion transformation serving layers

Manage schema evolution, data contracts, and pipeline reliability

Distributed Systems & Scale

Work on systems handling high-volume graph datasets (entities + relationships)

Optimize for latency, throughput, and fault tolerance

Design scalable architectures using Kafka / Spark / Flink / Beam

Cloud & Infrastructure

Deploy and operate systems on GCP / AWS (GKE, Dataproc, Cloud Run, etc.)

Build and maintain CI/CD pipelines for data and microservices

Use Docker, Kubernetes, Terraform for infrastructure automation

Data Reliability & Observability

Implement data quality checks, monitoring, and alerting

Ensure data integrity across pipelines and services

Build systems to detect drift, inconsistencies, and failures in production

APIs & System Integration

Work with GraphQL / REST / gRPC APIs for data access layers

Ensure seamless integration between data systems and application layers

What We’re Looking For

You have atleast 4 years in Data Engineering / Platform Engineering / Distributed Systems

Strong hands-on experience with: Apache Spark / Distributed data processing, Cloud platforms (GCP or AWS), Streaming systems (Kafka / Flink / Beam)

Solid programming skills in Python / Java / Scala / Node.js

Experience building and owning production data pipelines end-to-end

Understanding of: Microservices architecture, Data modeling & large-scale system design

Ability to debug and optimize systems in real production environments

Why This Role is Different

You own systems, not just components

You work on real scale (millions billions of data points)

You solve distributed systems + graph + real-time problems

You operate close to production impact, not isolated dev work

You influence architecture from day one in an early-stage environment

What You’ll Get

High ownership, low bureaucracy environment

Work on cutting-edge graph + AI-driven data systems

Exposure to complex, real-world data problems (fraud, risk, intelligence)

Fast growth with direct impact on core platform architecture