Senior Software Engineer (Data Engineer)

Jobgether; India; On-site; INR 3,000,000 - 5,000,000; Full time

Job summary

Jobgether is seeking a Senior Software Engineer (Data Engineer) based in India to build scalable data infrastructure powering analytics, ML, and AI-driven experiences. You will work on distributed systems, high-volume pipelines, and modern analytical platforms, owning solutions from design to deployment and monitoring.

The ideal candidate has 5+ years of backend and data engineering experience, with strong SQL, cloud (GCP), and multi-tenant SaaS governance.

Qualifications

• 5+ years of hands-on software engineering experience building and operating highly scalable distributed systems, data engineering solutions, or backend services in production.
• Strong backend engineering experience is mandatory, including hands-on development and maintenance of production-grade backend services.
• Solid understanding of system design and the ability to make sound technical, architectural, and design decisions.
• Strong experience with modern analytical data warehouses such as ClickHouse, BigQuery, Snowflake, or Amazon Redshift.
• Deep expertise in ClickHouse, including its internals, materialized views, and OLAP workload optimization, is an advantage.
• Experience designing and operating large-scale data ingestion pipelines using Kafka, Pulsar, CDC-based architectures, and related streaming technologies.
• Familiarity with Debezium, Flink, Dataflow, or comparable streaming and data-processing frameworks is preferred.
• Strong SQL skills, including data modeling, query optimization, and analytical workloads.
• Experience delivering data engineering or analytics engineering solutions involving large-scale data processing and transformation.
• Experience building and maintaining backend services in Go or another modern strongly typed programming language, together with proficiency in Python.
• Experience with cloud platforms, preferably GCP, including managed data, messaging, and observability services.
• Experience working with multi-tenant SaaS platforms, data governance, data security controls, and infrastructure-as-code tools such as Terraform.
• Experience with analytical or time-series databases such as PostgreSQL, TimescaleDB, or similar technologies.
• Exposure to AI-powered applications, LLM integrations, or agentic workflows is an added advantage.
• Strong problem-solving, communication, ownership, and collaboration skills, with the ability to work independently and effectively across multidisciplinary teams.
• Comfortable participating in on-call rotations and focused on building straightforward, efficient solutions rather than over-engineering.
• A customer-focused, results-oriented mindset with a strong commitment to urgency, excellence, resourcefulness, and continuous improvement.

Responsibilities

• Design, build, and operate scalable data engineering services supporting analytics, reporting, machine learning, and AI workloads across multiple products.
• Develop and maintain reliable data ingestion pipelines using CDC, event-driven architectures, Kafka, Pulsar, and related streaming technologies.
• Build and continuously improve a centralized analytics warehouse, optimizing it for performance, scalability, reliability, and maintainability.
• Design data models, materialized views, aggregation strategies, and analytical structures that support product analytics and business reporting.
• Develop backend APIs and services that expose analytics and reporting capabilities to internal and external consumers.
• Define and implement multi-tenant security controls, data governance standards, access management policies, and infrastructure-as-code practices.
• Monitor data pipeline health, freshness, ingestion lag, system performance, and overall reliability, participating in on-call rotations when required.
• Contribute to technical specifications, architecture decisions, system design discussions, and engineering standards.
• Improve developer productivity through automation, tooling, observability, testing, and operational excellence.
• Strengthen test coverage and engineering practices to ensure production systems remain reliable, maintainable, and easy to evolve.
• Own production systems end to end, from initial technical design and stakeholder alignment through deployment, operational support, and iterative improvements.
• Collaborate effectively with technical and non-technical stakeholders while delivering simple, efficient solutions without unnecessary complexity.

Skills

Backend engineering

Distributed systems

Data pipelines

ClickHouse

BigQuery

Snowflake

Redshift

Kafka

Pulsar

CDC architectures

Debezium

Flink

Dataflow

SQL proficiency

Go

Python

GCP

Terraform

PostgreSQL

TimescaleDB

AI/LLM integration

Ownership

On-call

Multi-tenant SaaS

Data governance

Data security controls

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Software Engineer (Data Engineer) based in India.

This is an opportunity to build scalable data infrastructure that powers analytics, reporting, machine learning, and AI-driven experiences across a growing product ecosystem. You will work on complex distributed systems, high-volume data pipelines, and modern analytical platforms that support both internal and external users. The role combines hands‑on backend engineering with data architecture, system design, security, and operational excellence. You will have significant ownership, taking solutions from technical design through deployment, monitoring, and continuous improvement. Collaboration across technical and business teams will be essential as you solve challenging data problems with minimal dependencies. The ideal candidate is a proactive builder who values simplicity, reliability, customer impact, and continuous innovation.

Accountabilities

• Design, build, and operate scalable data engineering services supporting analytics, reporting, machine learning, and AI workloads across multiple products.
• Develop and maintain reliable data ingestion pipelines using CDC, event‑driven architectures, Kafka, Pulsar, and related streaming technologies.
• Build and continuously improve a centralized analytics warehouse, optimizing it for performance, scalability, reliability, and maintainability.
• Design data models, materialized views, aggregation strategies, and analytical structures that support product analytics and business reporting.
• Develop backend APIs and services that expose analytics and reporting capabilities to internal and external consumers.
• Define and implement multi‑tenant security controls, data governance standards, access management policies, and infrastructure‑as‑code practices.
• Monitor data pipeline health, freshness, ingestion lag, system performance, and overall reliability, participating in on‑call rotations when required.
• Contribute to technical specifications, architecture decisions, system design discussions, and engineering standards.
• Improve developer productivity through automation, tooling, observability, testing, and operational excellence.
• Strengthen test coverage and engineering practices to ensure production systems remain reliable, maintainable, and easy to evolve.
• Own production systems end to end, from initial technical design and stakeholder alignment through deployment, operational support, and iterative improvements.
• Collaborate effectively with technical and non‑technical stakeholders while delivering simple, efficient solutions without unnecessary complexity.

Requirements

• 5+ years of hands‑on software engineering experience building and operating highly scalable distributed systems, data engineering solutions, or backend services in production.
• Strong backend engineering experience is mandatory, including hands‑on development and maintenance of production‑grade backend services.
• Solid understanding of system design and the ability to make sound technical, architectural, and design decisions.
• Strong experience with modern analytical data warehouses such as ClickHouse, BigQuery, Snowflake, or Amazon Redshift.
• Deep expertise in ClickHouse, including its internals, materialized views, and OLAP workload optimization, is an advantage.
• Experience designing and operating large‑scale data ingestion pipelines using Kafka, Pulsar, CDC‑based architectures, and related streaming technologies.
• Familiarity with Debezium, Flink, Dataflow, or comparable streaming and data‑processing frameworks is preferred.
• Strong SQL skills, including data modeling, query optimization, and analytical workloads.
• Experience delivering data engineering or analytics engineering solutions involving large‑scale data processing and transformation.
• Experience building and maintaining backend services in Go or another modern strongly typed programming language, together with proficiency in Python.
• Experience with cloud platforms, preferably GCP, including managed data, messaging, and observability services.
• Experience working with multi‑tenant SaaS platforms, data governance, data security controls, and infrastructure‑as‑code tools such as Terraform.
• Experience with analytical or time‑series databases such as PostgreSQL, TimescaleDB, or similar technologies.
• Exposure to AI‑powered applications, LLM integrations, or agentic workflows is an added advantage.
• Strong problem‑solving, communication, ownership, and collaboration skills, with the ability to work independently and effectively across multidisciplinary teams.
• Comfortable participating in on‑call rotations and focused on building straightforward, efficient solutions rather than over‑engineering.
• A customer‑focused, results‑oriented mindset with a strong commitment to urgency, excellence, resourcefulness, and continuous improvement.

Benefits

• Opportunity to work on scalable data platforms supporting analytics, machine learning, and AI‑powered applications.
• High level of ownership across the complete engineering lifecycle, from architecture and development through production operations.
• Exposure to modern cloud, streaming, analytical database, observability, and AI technologies.
• Opportunity to solve complex distributed‑systems and data‑engineering challenges in a product‑driven environment.
• Collaborative work with technical and non‑technical stakeholders across a growing technology organization.
• Professional growth through exposure to large‑scale systems, advanced engineering practices, and evolving data technologies.

How Jobgether works:

We use an AI‑powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top‑fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre‑contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.