Senior Software Engineer, Data Platform & Backend

RedGraphs, Inc.; Delhi; Hybrid; INR 2,500,000 - 4,200,000; Full time

Job summary

RedGraphs, Inc. is building production Python and JVM backends and distributed data systems to deliver time-aware economic relationship data at scale from Delhi NCR.

You will own design of dependable software, data models, and pipelines, with emphasis on correctness, changelog lineage, and cross-time consistency, while collaborating with US teams. We are a small engineering team focused on robust data processing, verifiable evidence, and safe, replayable workflows.

Qualifications

• Strong senior level software engineering ownership.
• Typically six or more years of relevant professional experience.
• Strong production engineering in Python, Java or Scala for backend services.
• Strong SQL and relational data modeling skills.
• Experience designing backend services, distributed processing systems, or data-intensive applications.

Responsibilities

• Design and build production Python services, APIs, workers, and processing systems.
• Build reliable batch, asynchronous, and distributed workflows for document and relationship processing.
• Design clear interfaces between extraction, validation, storage, analytical, and delivery systems.
• Build and evolve data models for entities, relationships, evidence, historical state, corrections, and publication.
• Preserve point-in-time semantics, and distinguish when an economic relationship existed from when information about it became available.
• Design safe schema migrations and backward compatible data changes.
• Build idempotent, replayable, recoverable processing workflows.
• Prevent duplicate processing, partial publication, and silent data loss.
• Preserve lineage from source evidence through intermediate processing to the final data products.
• Build validation and reconciliation systems that catch inconsistencies before publication.
• Work with relational databases, object storage, partitions, queues, caches, and cloud services.
• Improve query performance and processing throughput without compromising correctness.
• Build observability around long-running and distributed workflows.
• Design dependable retry, checkpointing, timeout, and failure recovery mechanisms.
• Improve deployment, CI/CD, testing, and production verification.
• Support reliable dataset and API delivery to downstream systems and institutional workflows.
• Work closely with AI engineers so that probabilistic model outputs enter deterministic production systems safely.
• Ensure downstream analytical systems can consume historical relationship data without losing temporal or provenance semantics.

Skills

Python

Java

Scala

SQL

Distributed systems

REST APIs

CI/CD

Linux

Docker

AWS

Tools

Git

PostgreSQL

Linux

Docker

Job description

Industrialize the data. Build production backend and distributed data systems that make evidence-backed economic relationship data reproducible, point-in-time correct, and reliably deliverable at scale.

Location: Delhi NCR, India

Employment type: Full time

Team: Engineering

Working Model

This role is based in Delhi NCR, India.

RedGraphs is building its India engineering team in Delhi NCR and plans to establish a collaborative development office there. The team currently works remotely while that office is being established.

Candidates should be based in, or willing to relocate to, Delhi NCR and comfortable with regular in-person collaboration once the office is operational.

The role also requires meaningful and consistent working overlap with the United States Eastern time zone.

About RedGraphs

RedGraphs builds economic intelligence infrastructure that makes business relationships visible, measurable, and actionable.

We transform evidence buried in regulatory filings, financial disclosures, and other primary source corporate documents into structured, time-aware relationship data.

Our platform spans three connected layers. RedGraphs Assist governs how economic relationships are captured and verified from source evidence. Business Relationships Analytics, jointly developed with S&P Global Market Intelligence and commercially licensed and delivered through S&P Global Market Intelligence, provides structured supplier and customer relationship data with historical depth. RedGraphs Vista operates above the relationship graph to analyze multi-tier dependencies, economic exposure, concentration, and changing network structure.

Accuracy matters at every layer. A technically valid result can still be wrong if the evidence, entity attribution, relationship direction, economic magnitude, or timing is wrong. Our systems therefore combine machine intelligence, deterministic software, temporal data infrastructure, graph analytics, and governed human verification.

We are a small engineering team. Senior engineers are expected to understand the business meaning of the systems they build and to own important problems from investigation through production verification.

About the Role

Once RedGraphs understands a relationship in a source document, that relationship has to move through processing, validation, storage, historical versioning, analytics, and delivery without losing its meaning.

That requires much more than conventional ETL.

Relationships can be corrected. Evidence can arrive later. Historical facts can describe earlier economic periods. The same dataset has to support current production workflows and point-in-time reconstruction, without contaminating history with information that was not yet known.

You will build the backend and data systems that make this possible.

This role sits at the intersection of backend software engineering, data infrastructure, distributed processing, and cloud systems.

It is not a data analyst role, and it is not primarily infrastructure administration. The core responsibility is designing dependable software and data systems.

What Success Looks Like

During the first 90 days, you should be able to:

• Understand the path from extracted evidence to stored and delivered relationship data.
• Reproduce a meaningful data, backend, or distributed processing failure.
• Identify the invariant that the system was supposed to preserve.
• Ship a contained reliability, correctness, or performance improvement with automated tests.
• Demonstrate that the change is replayable and safe against historical data.
• Begin independently owning a material service, processing workflow, or data subsystem.

Over time, you should become a technical owner of an important part of the RedGraphs production data platform.

How We Work

Evidence before assertion: Important technical conclusions should be supported by code, data, tests, or directly observed behavior.

Correctness without artificial slowness: We move quickly, but not by hiding uncertainty or bypassing validation.

Ownership beyond implementation: Engineers follow changes through testing, deployment, verification, and operational outcomes.

Independent thinking: Senior engineers are expected to challenge assumptions and recommend better approaches when the evidence supports them.

Written first: Investigations, decisions, architecture, and operational knowledge should remain reviewable by other people.

Historical integrity matters: Improving current behavior must not silently change what historical data means, or what information was knowable at a given point in time.

This Role Is Not

This is not primarily:

• dashboard development
• analytics engineering
• DevOps administration
• ticket-driven ETL development
• maintaining CRUD APIs without deeper system ownership

We are looking for a software engineer who thinks in invariants, failure modes, recovery paths, data lineage, and production correctness.

Responsibilities

• Design and build production Python services, APIs, workers, and processing systems.
• Build reliable batch, asynchronous, and distributed workflows for document and relationship processing.
• Design clear interfaces between extraction, validation, storage, analytical, and delivery systems.
• Build and evolve data models for entities, relationships, evidence, historical state, corrections, and publication.
• Preserve point-in-time semantics, and distinguish when an economic relationship existed from when information about it became available.
• Design safe schema migrations and backward compatible data changes.
• Build idempotent, replayable, recoverable processing workflows.
• Prevent duplicate processing, partial publication, and silent data loss.
• Preserve lineage from source evidence through intermediate processing to the final data products.
• Build validation and reconciliation systems that catch inconsistencies before publication.
• Work with relational databases, object storage, partitions, queues, caches, and cloud services.
• Improve query performance and processing throughput without compromising correctness.
• Build observability around long-running and distributed workflows.
• Design dependable retry, checkpointing, timeout, and failure recovery mechanisms.
• Improve deployment, CI/CD, testing, and production verification.
• Support reliable dataset and API delivery to downstream systems and institutional workflows.
• Work closely with AI engineers so that probabilistic model outputs enter deterministic production systems safely.
• Ensure downstream analytical systems can consume historical relationship data without losing temporal or provenance semantics.

Required qualifications

• Strong evidence of senior level software engineering ownership.
• Typically six or more years of relevant professional experience, or equivalent evidence of senior technical scope and ownership.
• Strong production engineering in Python, Java, Scala, or another comparable backend language, with the ability to work effectively in Python.
• Strong SQL and relational data modeling skills.
• Strong experience designing backend services, APIs, distributed processing systems, or data-intensive applications.
• Experience with asynchronous processing, queues, workers, workflow orchestration, or large scale batch systems.
• Experience designing systems for idempotency, retries, failure recovery, and safe reprocessing.
• Strong automated testing practices.
• Experience with Git, Linux, Docker, CI/CD, and production debugging.
• Strong experience building and operating production systems on AWS or another major cloud platform.
• Ability to understand existing production systems independently and reason carefully about data correctness.
• Strong written technical communication.

Preferred qualifications

• Production experience with Java or Scala, particularly in distributed, data-intensive, or high-throughput systems.
• Hands-on AWS experience with services such as S3, EC2, ECS, Lambda, SQS, Step Functions, RDS and PostgreSQL, Athena, Glue, and CloudWatch, or equivalent services elsewhere.
• Temporal databases, bitemporal data, event sourcing, change data models, or point-in-time datasets.
• Data lineage and provenance systems.
• Large scale object storage or data lake architectures.
• Partitioned analytical datasets.
• Workflow orchestrators and distributed job processing frameworks.
• Data quality, reconciliation, or publication control systems.
• Financial or market data.
• Enterprise data products with strong auditability requirements.
• Processing large collections of documents or unstructured data.
• Relationship oriented datasets.
• Infrastructure as code and cloud observability.
• Performance and cost optimization of cloud data systems.