About the role

We are looking for a Lead Data Engineer to build and scale Arnor’s data systems. You will lead the design and delivery of reliable data platforms across batch and migration pipelines, streaming and event-driven workloads, and analytics, lakehouse, and warehouse use cases.

This is a hands-on leadership role. You will set technical direction, work closely with product and platform teams, and manage a small team of engineers. Our platform is GCP-first, while some client deployments require other cloud environments. You will help us choose data technologies that fit the workload and deployment context rather than assume one processing or streaming tool for every use case.

What you’ll do

● Define and evolve the architecture and roadmap for Arnor’s data systems, balancing scale, reliability, performance, security, and cost.

● Design, build, and operate batch ingestion, transformation, migration, reconciliation, and historical backfill pipelines.

● Develop event-driven and streaming data workflows with clear delivery guarantees, idempotency, retry and dead-letter handling, replay, and operational visibility.

● Build analytics and warehouse/lakehouse capabilities, including robust data models and dependable access patterns for product and reporting needs.

● Establish practical standards for data quality, provenance, freshness, coverage, lineage, and recoverability. Partner with product and platform teams on shared ownership of these capabilities.

● Design pipelines to retain and trace source data where appropriate, surface partial results and processing failures, and support safe reprocessing without unnecessary source calls.





● Build systems that respect tenant and project boundaries, access controls, and client deployment constraints.

● Set service interfaces and data contracts with product, platform, and application teams; keep downstream business logic and user-facing behavior with the owning product teams.

● Evaluate and evolve the data stack for GCP-first deployments and client environments on other clouds. Use BigQuery where it fits; select processing and streaming technologies based on workload and deployment requirements.

● Lead, coach, and support a small engineering team through technical planning,

design reviews, delivery, and ongoing development.

● Stay hands-on through architecture work, code and data-model reviews, and implementation of critical components.

What you’ll bring

● Strong experience designing and operating production data systems across batch pipelines, migrations, event-driven workloads, and analytics.

● Hands-on proficiency with SQL, Python, and Node.js.

● Hands-on experience building or operating data workloads on Google Cloud

Platform (GCP).

● At least one current Google Cloud certification relevant to cloud or data engineering.

● Experience with data modeling, pipeline reliability, data quality, and production operations,



including monitoring, failure recovery, backfills, and performance or cost tuning.

● Ability to design systems that are secure, maintainable, and suitable for multi-tenant or customer-specific deployments.

● Technical leadership experience, including guiding engineers, making architecture decisions, and delivering through a team.

● Clear communication and the ability to work across engineering, product, and platform teams.

● Willingness to work full time onsite in Chandigarh.

Preferred qualifications

● Experience with BigQuery and GCP data services.

● Experience supporting data platforms deployed on AWS, Azure, or other client-managed cloud environments.

● Experience with PostgreSQL, high-volume data migrations, or multi-tenant platforms.

● Experience with warehouse or lakehouse architectures and large-scale data processing.

● Experience improving source-data coverage, provenance, data quality, and protected reprocessing across multiple products.

● Additional certifications in other cloud platforms, data engineering, or related technologies.

We are open to candidates from different industries and domains. Legal or court-data experience is not required.

Recommended experience

We recommend 8–12 years of relevant data or platform engineering experience,

including at least 3 years in technical leadership or engineering team leadership. This range should support the role’s combination of hands-on system design, production delivery, and people management while leaving room for strong candidates with different career paths.

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