Metasys
Pay not stated in the advert
Overview: Data Strategy and Governance
The Data Architect is responsible for designing and governing the end-to-end data landscape of our integrated supply chain e-commerce platform. You will create comprehensive data models, establish robust ETL pipelines to unify data from core systems (MES, WMS, OMS), and architect the infrastructure for real-time analytics, ensuring strict compliance with data privacy regulations like GDPR.
Internship Details
Duration: 3 months
Start Date: Immediate
Location: Remote
Stipend: None initially. Based on your first-quarter performance, you may be offered a paid full-time opportunity, or even be absorbed directly by the client as an FTE.
Key Responsibilities & Core Projects
You will transform raw system data into a structured asset, supporting both operational efficiency and business intelligence.
Data Modeling & Design: Design and maintain comprehensive conceptual, logical, and physical data models for all data domains: e-commerce transactions, customer behavior, internal tool usage, and supply chain records.
Data Integration (ETL): Architect and implement scalable ETL/ELT pipelines to efficiently aggregate, transform, and load data from disparate sources, including PostgreSQL 15 (MES, WMS, OMS modules) and internal applications.
Data Governance & Compliance: Define and enforce data governance policies, focusing heavily on GDPR compliance, data privacy, access control, and data quality standards.
Analytics Infrastructure: Design and implement the infrastructure for real-time analytics and business intelligence, ensuring data is readily available and optimized for consumption by reporting tools and the Analytics/BI module.
Retention & Archival: Define and plan data retention, archiving, and purging strategies, ensuring long-term data management efficiency and compliance.
Database Optimization: Collaborate with SRE and development teams to optimize database schemas and queries for both high transactional throughput and analytical reporting performance.
Required Technologies & Tools
Candidates must possess deep experience in data modeling, integration, and governance across diverse systems:
Database Mastery: Expert proficiency in PostgreSQL 15 (schema design, performance tuning, RLS principles) and caching technologies (Redis).
Data Warehousing/ETL: Proven experience designing and implementing scalable data pipelines and data warehouse structures.
Programming/Scripting: Proficiency in scripting for data transformation and pipeline automation (e.g., Python, SQL).
Compliance: Mandatory experience implementing controls for GDPR and other data privacy regulations.
Search/Storage: Familiarity with data usage in Meilisearch and object storage systems (MinIO).
AI Agent Focus
You will structure the data assets required to power and train our AI layer.
Data for LLMs: Architect the data flow and preparation layer that provides clean, contextually relevant data for LLM fine-tuning, prompt engineering, and Retrieval Augmented Generation (RAG) processes.
Interaction Modeling: Design models to capture and track AI agent interactions and outcomes for auditing, performance analysis, and continuous improvement of the multi-agent system.
Success Metrics & Career Path
Performance will be measured by:
Data Accuracy/Quality: Measurable improvement in the quality, consistency, and reliability of data used for analytics.
Pipeline Efficiency: Speed, stability, and latency of ETL pipelines supporting real-time analytics requirements.
Compliance Audit: Successful implementation and auditing of data governance and GDPR policies.
Mentorship Structure: Reports to the Solution Architect or Head of Technology, working closely with the Analytics/BI, Security, and Core Module development teams.
Originally posted on Himalayas
