The Role
Build and operate the NEXUS data platform supporting analytics, operational data services, and AI/ML products. Responsibilities include managing Apache Iceberg lakehouse operations, CDC and batch/streaming ingestion, distributed compute optimization, data quality and governance, snapshot and SCD publishing, pipeline orchestration, APIs, observability, and GitOps/IaC deployments. The role requires strong Apache Spark, lakehouse, data modeling, Python, containerization, and infrastructure engineering expertise.
Summary Generated by Built In
Hyderabad, India
There’s a saying at Itineris: Together goes a long way. And we live by that. Whether your job is translating our customers’ needs into the best software, selling, coding, or keeping our business running smoothly, we’re united by the same mission: growing our company and delivering innovative software that empowers energy and water utilities to engage better with their customers.
UMAX is the brand name of our software solution for water and energy utilities. It automates their business processes and enables them to better engage with their customers. UMAX is a cloud-based solution, built on the powerful Microsoft Dynamics 365 platform.
Role Overview
We are building a secure, scalable, and product-oriented data platform, called NEXUS, that becomes the backbone for analytics, operational data services, and AI/ML products. This role accelerates how our teams ingest, govern, activate, and innovate with data, reducing time-to-insight and enabling AI-driven applications responsibly.
As a Data Platform Engineer
You’ll work closely be a part of a small, five-person Data & AI platform team, and partner with the AI Practice and UMAX Product / Engineering teams, with an initial focus on:
• Own our Apache Iceberg lakehouse end to end. REST catalog (Polaris) operations, table maintenance (compaction, snapshot expiry, orphan cleanup, sort clustering etc.), storage layout, and the merge-on-read vs copy-on-write tradeoff per table.
• Building and operating CDC / replication and batch + streaming ingestion pipelines for analytics, operational, and AI/ML consumers.
• Tuning the cost and performance of distributed compute and storage (incremental vs full rewrites, spot capacity, autoscaling, file sizing).
• Provisioning data-specific features (e.g. databases, feature stores, model registries)
• Deliver version-aware snapshots / SCD publish (cross-window deduplication, deterministic and auditable snapshot assembly, merges / upserts into well-contracted consumer stores).
• Enforcing data quality, lineage, and access / classification controls across batch & streaming.
• Orchestrating and operating scheduled, idempotent pipeline runs (failure recovery, retries).
• Offering well-contracted data stores and access APIs, built for portability across our multi-cloud (SaaS / hybrid / federated) footprint.
• Ship via GitOps and IaC (declarative deployment (ArgoCD / Helm), infrastructure-as-code (Bicep) — owning the pipeline, not just the code).
Qualifications
• 5+ years in data infrastructure, platform engineering, or related backend roles.
Deep knowledge of:
◦ Deep Apache Spark internals: partitioning, skew, file sizing, join strategies, fluency in reading plans
◦ Lakehouse / warehouse design (Iceberg / Delta)
◦ Efficient storage design (partitioning, Z-ordering, compaction)
◦ Data replication & CDC, and batch + streaming ingestion (e.g. Kafka / Event Hubs / IoT Hub)
◦ Schema & data contract governance
◦ SQL & analytical data modelling (current state, SCD, merges / upserts, dimensional modelling)
• Proficient in Python, with strong software engineering and containerized application development (e.g. Docker / Kubernetes); comfortable building well-tested, modular, performant code. Infrastructure-as-code familiarity is a plus but secondary.
• Observability mindset: metrics, tracing, and logging for data systems.
Nice to Have
• Operationalizing ML data: feature stores, model / feature serving contracts, drift / monitoring data (modeling itself stays with the AI Practice).
• Building prediction-serving, write-back, and feedback-label pipelines that connect model outputs back into UMAX (the NEXUS integration layer), to support AI/ML initiatives.
• Document stores (Cosmos DB, MongoDB).
• Experience with regulated / utility customer data.
What we offer
There’s a saying at Itineris: Together goes a long way. And we live by that. Whether your job is translating our customers’ needs into the best software, selling, coding, or keeping our business running smoothly, we’re united by the same mission: growing our company and delivering innovative software that empowers energy and water utilities to engage better with their customers.
UMAX is the brand name of our software solution for water and energy utilities. It automates their business processes and enables them to better engage with their customers. UMAX is a cloud-based solution, built on the powerful Microsoft Dynamics 365 platform.
Role Overview
We are building a secure, scalable, and product-oriented data platform, called NEXUS, that becomes the backbone for analytics, operational data services, and AI/ML products. This role accelerates how our teams ingest, govern, activate, and innovate with data, reducing time-to-insight and enabling AI-driven applications responsibly.
As a Data Platform Engineer
You’ll work closely be a part of a small, five-person Data & AI platform team, and partner with the AI Practice and UMAX Product / Engineering teams, with an initial focus on:
• Own our Apache Iceberg lakehouse end to end. REST catalog (Polaris) operations, table maintenance (compaction, snapshot expiry, orphan cleanup, sort clustering etc.), storage layout, and the merge-on-read vs copy-on-write tradeoff per table.
• Building and operating CDC / replication and batch + streaming ingestion pipelines for analytics, operational, and AI/ML consumers.
• Tuning the cost and performance of distributed compute and storage (incremental vs full rewrites, spot capacity, autoscaling, file sizing).
• Provisioning data-specific features (e.g. databases, feature stores, model registries)
• Deliver version-aware snapshots / SCD publish (cross-window deduplication, deterministic and auditable snapshot assembly, merges / upserts into well-contracted consumer stores).
• Enforcing data quality, lineage, and access / classification controls across batch & streaming.
• Orchestrating and operating scheduled, idempotent pipeline runs (failure recovery, retries).
• Offering well-contracted data stores and access APIs, built for portability across our multi-cloud (SaaS / hybrid / federated) footprint.
• Ship via GitOps and IaC (declarative deployment (ArgoCD / Helm), infrastructure-as-code (Bicep) — owning the pipeline, not just the code).
Qualifications
• 5+ years in data infrastructure, platform engineering, or related backend roles.
Deep knowledge of:
◦ Deep Apache Spark internals: partitioning, skew, file sizing, join strategies, fluency in reading plans
◦ Lakehouse / warehouse design (Iceberg / Delta)
◦ Efficient storage design (partitioning, Z-ordering, compaction)
◦ Data replication & CDC, and batch + streaming ingestion (e.g. Kafka / Event Hubs / IoT Hub)
◦ Schema & data contract governance
◦ SQL & analytical data modelling (current state, SCD, merges / upserts, dimensional modelling)
• Proficient in Python, with strong software engineering and containerized application development (e.g. Docker / Kubernetes); comfortable building well-tested, modular, performant code. Infrastructure-as-code familiarity is a plus but secondary.
• Observability mindset: metrics, tracing, and logging for data systems.
Nice to Have
• Operationalizing ML data: feature stores, model / feature serving contracts, drift / monitoring data (modeling itself stays with the AI Practice).
• Building prediction-serving, write-back, and feedback-label pipelines that connect model outputs back into UMAX (the NEXUS integration layer), to support AI/ML initiatives.
• Document stores (Cosmos DB, MongoDB).
• Experience with regulated / utility customer data.
What we offer
- A fast-growing and international environment with offices in Hyderabad (India), Ghent (Belgium), Atlanta (USA), London (UK).
- Working with future-ready water and energy companies and building valuable utility expertise.
- Internal mobility, developing your career through various educational programs and growth paths.
- An entrepreneurial mindset where you have a direct impact on our growth journey.
- An open environment where we understand the importance of taking a break to recharge and achieve the best results. We make it a priority to celebrate milestones, both big and small.
- Onboarding, learning and buddy programs to get ready for a smooth start in your new job.
- A flexible and inclusive working environment – where people can just be who they are, and where we offer flexible options for hybrid working so you can work in a way that suits you best.
- Competitive perks & benefits, because we want to help you reach your full potential, both at work and in your personal life.
- A lot of fun moments with an eye for sports, celebration and wellbeing, important for maintaining a healthy work-life balance.
Skills Required
- 5+ years of experience in data infrastructure, platform engineering, or related backend roles
- Deep knowledge of Apache Spark internals, including partitioning, skew, file sizing, join strategies, and query-plan analysis
- Experience with lakehouse or warehouse design using Apache Iceberg or Delta Lake
- Knowledge of efficient storage design, including partitioning, Z-ordering, and compaction
- Experience with data replication, CDC, and batch and streaming ingestion
- Knowledge of schema and data contract governance
- Proficiency in SQL and analytical data modeling, including SCD, merges, upserts, and dimensional modeling
- Proficiency in Python and strong software engineering practices
- Experience with containerized application development, such as Docker or Kubernetes
- Ability to build well-tested, modular, and performant code
- Observability mindset covering metrics, tracing, and logging for data systems
- Infrastructure-as-code familiarity
- Experience operationalizing ML data, feature stores, model or feature serving contracts, and drift-monitoring data
- Experience building prediction-serving, write-back, and feedback-label pipelines
- Experience with document stores such as Cosmos DB or MongoDB
- Experience with regulated or utility customer data
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The Company
What We Do
Itineris is a software company focused exclusively on water and energy utilities. It develops and implements UMAX, a cloud-based Customer Information System, CRM, ERP, asset, and field-service management platform built on Microsoft Dynamics 365 and delivered through Azure. The solution uses workflow automation, AI, and data capabilities to improve utility operations, customer engagement, revenue management, and environmental performance. Itineris serves utility organizations internationally.
