Data Platform Engineer

Аналитика рынка

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Описание

About the company:  Damia Group is an international tech recruitment agency with 3 decades of experience. Our arrival in Portugal, 7 years later, was set on a mission to transform IT recruitment experiences and, through them, achieve better results. We believe in long-term relationships with a transparent and relaxed mindset. In a short period, we have reached the hearts of both scale-ups and larger organisations by delivering spot-on curated candidate shortlists, increased job offer acceptance rates and shorter time-to-fill. Role Overview This is a senior, hands-on role for someone who views Data Infrastructure as a product. You will define how billions of records are structured, indexed, and exposed to the rest of the company. Ideal for someone who combines strong backend engineering skills with deep expertise in data platforms, data lakes, and large-scale data systems.   Key Responsibilities Database Reliability & Scaling: Own the health and performance of their core databases. Own and optimize their MongoDB clusters and OpenSearch indexes, which houses billions of documents. You will design sharding strategies and indexing patterns to ensure search performance. Design and implement a multi-tier storage strategy. You will determine which data remains "hot" in production databases for their Product team and which data is offloaded to "cold/analytical" storage for AI and R&D. Data Access Layer: Build and maintain internal APIs that allow internal teams to build features without worrying about the underlying database complexities. Schema Evolution & Migrations: Lead the strategy for updating data structures across billions of records. You will design "no-downtime" migration paths for their production MongoDB and OpenSearch environments. Data Platform & Architecture Own and evolve their Core Data Architecture, ensuring it supports analytics, product features, AI workflows, and internal consumption. Evaluate the feasibility and ROI of introducing a Data Lakehouse architecture for long-term storage and AI training. Define standards for how data is ingested, stored, versioned, and exposed to downstream systems. Data Quality & Documentation: Ensure data accuracy, freshness, and consistency through validation and testing. Maintain clear, up-to-date documentation of pipelines, schemas, and data assets to enable internal adoption. g. data enrichment) are integrated into

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