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Zinier

Cloud-based AI-driven field service management platform

Senior Data Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, IndiaNo salary listed

About the Job

Requirements

  • 5–10 years of data engineering experience in a product or SaaS company, with a proven track record building production data platforms, warehouses, or lake architectures from the ground up.
  • Strong data modelling fundamentals, including designing normalised (3NF) operational schemas and dimensional/star schemas for analytics, and navigating schema divergence across multi-tenant deployments.
  • Fluency in CDC, ELT/ETL patterns, job orchestration, idempotent pipelines, and schema evolution handling.
  • Strong programming skills in Python, Go, or Java, with hands-on experience building and maintaining data pipelines, transformation logic, and automation in production environments.
  • Deep AWS data stack expertise with hands-on experience in S3, Glue (ETL and Data Catalog), DMS, Redshift or Redshift Serverless, Athena, and QuickSight, or comparable experience with equivalent tools and willingness to ramp quickly.
  • Experience implementing data cataloguing, lineage tracking, access controls, and change management processes.
  • Ability to work across Data, Product, Solutions, and AI teams and distil technical complexity into clear decisions for non-technical stakeholders.
  • Based in India, ideally Bangalore, and able to collaborate with engineering, data, and product teams across time zones as needed.

Responsibilities

  • Design and own the canonical data model by co-creating the normalised ERD (3NF) with Solutions and Product, defining naming conventions, data types, and relationship standards for all deployments.
  • Build the Bronze-to-Silver-to-Gold medallion data platform on AWS using S3, DMS, Glue, Redshift Serverless, and QuickSight, from CDC ingestion through self-serve dashboards.
  • Develop and maintain CDC pipelines and ETL jobs that ingest data from Amazon RDS, apply business logic, calculate KPIs, and produce clean, analytics-ready star schemas.
  • Migrate client schemas to the standard model, starting with a pilot account and scaling across accounts with row-level tenant isolation while preserving client-specific fidelity.
  • Build Gold-layer flat marts and analytics-ready schemas for Customer Success self-serve QuickSight dashboards, enabling QBR materials to be generated in under two days without specialist involvement.
  • Establish data governance processes, including schema change control, a data dictionary, cataloguing standards via Glue Data Catalog, and audit-ready data lineage.
  • Create clean historical data pipelines to support future AI use cases and cross-client benchmarking models.
  • Collaborate with Product Engineering on schema migration, Solutions on ERD validation and regression testing, and the AI team on model training data requirements.

Desired Qualifications

  • Prior experience in FSM, field operations, utilities, telecom, or industrial SaaS is a plus.

About the Company

Zinier

Cloud-based AI-driven field service management platform

Zinier provides a cloud-based platform for deskless field workers that uses AI to automate and optimize field service operations. It helps manage work orders, track assets, control inventory, schedule, and dispatch tasks. The platform targets businesses with extensive field service teams (e.g., telecoms, utilities, maintenance) and aims to improve efficiency, resource utilization, and customer satisfaction. The system works by automating routine tasks, optimizing resource allocation, and giving real-time visibility into field operations, typically via a subscription-based pricing model. Zinier differentiates itself by combining AI-driven automation with cloud-based accessibility to streamline deskless workflows and enable proactive service delivery at scale. The company goal is to transform field service operations into more productive, predictable, and responsive processes for its clients.

Company size
51-200
Company stage
Series C
Total funding
$120M
Headquarters
San Mateo, CA
Founded
2015

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