Senior Data Engineer - India

NinjaOne

1d•Remote

About The Position

We're looking for a Senior Data Engineer to design, build, and operate the reliable, scalable data pipelines powering analytics, AI/ML, and operational workloads. This is engineering that matters; production-grade ETL with real observability, rigorous testing discipline, and architectural decisions that scale. You'll work across the full stack of modern data systems, applying strong design principles to build pipelines that don't just run, but run reliably at scale. If you're passionate about data engineering done right, where monitoring isn't an afterthought, tests are non-negotiable, and system design is foundational, this is the opportunity for you! Location - Remote in India

Requirements

Strong experience building ETL/ELT pipelines for large-scale data platforms.
Good understanding of tiered data architectures (e.g., Bronze/Silver/Gold, medallion model) and how to apply them in production.
Hands-on experience with pipeline observability (metrics, logs, alerts, SLAs/SLOs).
Solid understanding of distributed systems and system design fundamentals.
Experience testing data pipelines, including data quality checks, regression testing, and failure scenarios.
Proficiency in one or more programming languages (e.g., Python, Java, Scala).
Experience with cloud platforms (AWS, Azure, or GCP).
Strong problem-solving and production debugging skills.
Applicants must hold valid working rights in India at the time of application.
We are unable to provide visa sponsorship for this role.

Nice To Haves

Experience with streaming platforms (Kafka, Pulsar, Kinesis).
Familiarity with data lakes, lakehouse architectures, or OLAP systems.
Experience with CI/CD for data pipelines and infrastructure-as-code.
Exposure to regulated or high-availability environments.

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines across batch and streaming workloads.
Implement and operate pipelines following a tiered data model (e.g., Bronze/Silver/Gold) to ensure clear data contracts, quality boundaries, and reusability.
Build pipelines that are observable by default, with strong metrics, logging, tracing, and alerting.
Implement data quality checks, validations, and automated tests at each data tier to ensure correctness, freshness, and reliability.
Apply strong system design principles to build fault-tolerant, scalable, and maintainable data systems.
Optimize pipeline performance, cost, and reliability through profiling, monitoring, and tuning.
Collaborate with platform, analytics, and ML teams to design well-modelled datasets for downstream consumers.
Participate in architecture and design reviews, contributing to data modelling, ingestion, and observability standards.
Troubleshoot production issues across pipelines and storage layers using logs, metrics, and traces.
Ensure data pipelines comply with security, governance, and compliance requirements.
Other duties as needed.