Data Architect Internship

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