Senior Data Architect

TalentHue- Careers


Date: 6 hours ago
City: Lahore
Contract type: Full time
Role Introduction

Designs and owns the overall data platform architecture across a hybrid (public cloud + on-prem) topology. Defines how data flows from source systems through the lakehouse to consumption layers, and ensures the platform is governable, scalable, and cost-efficient at enterprise scale.

Requirements
  • Define the end-to-end lakehouse architecture using Apache Iceberg, Parquet, and Trino as the core open table format and query layer.
  • Architect hybrid topology decisions, what runs on public cloud vs. on-prem, and how data and compute move between them.
  • Set standards for table design, partitioning strategy, schema evolution, and metadata/catalog governance across Iceberg tables.
  • Own technology selection and integration patterns for Informatica IDMC within the broader platform.
  • Define data governance frameworks: lineage, cataloging, access control, and policy enforcement across the open lakehouse.
  • Review and approve designs from Data Modelers, Data Engineers, and DataOps to ensure architectural consistency.
  • Lead capacity planning and cost/performance tradeoff decisions across storage tiers and compute engines.
  • Act as the primary technical point of contact with the KSA client's architecture and platform stakeholders.
Specifications
  • 8-10 years in data engineering/architecture, with demonstrable ownership of at least one large-scale lakehouse or data platform build.
  • Hands-on architectural experience with Apache Iceberg or a comparable open table format (Delta Lake, Hudi) at production scale.
  • Strong working knowledge of Trino/Presto-style federated query engines and Spark for distributed processing.
  • Direct exposure to Informatica IDMC (or equivalent enterprise integration/governance suite) in an architectural capacity.
  • Proven experience designing hybrid (cloud + on-prem) data topologies, not purely cloud-native environments.
  • Strong data modeling foundation, dimensional and/or Data Vault, even though hands-on modeling sits with the Data Modeler role.
  • Excellent stakeholder communication; able to translate architecture decisions for both engineering teams and client leadership.


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