Data Architect - Retail/E-commerce

Role Overview:

Data Architect

Key Responsibilities:

• Data Strategy & Architecture

• Define and implement enterprise-level

  data architecture, models, and pipelines

  to support business intelligence, personalization, and real-time decision-making.
• Develop

  data lake, data warehouse, and data mesh architectures

  for structured, semi-structured, and unstructured data.
• Create

  scalable data solutions

  for high-velocity retail/e-commerce environments (orders, supply chain, inventory, payments, customer journeys).

• Data Integration & Management

• Oversee the integration of

  data from multiple systems (ERP, CRM, POS, e-commerce platforms, marketing tools, logistics partners)

  .
• Ensure

  high availability, reliability, and scalability

  of data systems for millions of daily transactions.
• Define and enforce

  data governance, security, and compliance (GDPR, PCI-DSS, local regulations)

  .

• Advanced Analytics & Insights Enablement

• Collaborate with

  Data Scientists, Analysts, and Product Managers

  to enable AI/ML solutions (recommendation engines, demand forecasting, fraud detection, pricing optimization).
• Drive

  real-time analytics

  for customer behavior, supply chain visibility, and personalized marketing campaigns.

• Leadership & Collaboration

• Lead a team of data engineers and analysts, ensuring adoption of

  best practices

  in data modeling, ETL/ELT design, and cloud data management.
• Partner with stakeholders (Technology, Marketing, Finance, Operations) to align data architecture with business objectives.
  

Required Skills & Experience:

• Education:

  Bachelors/Master’s in Computer Science, Data Engineering, Information Systems, or related field.

• Experience:

  10+ years in

  data engineering, architecture, and analytics

  with at least 4+ years in

  retail/e-commerce domain

  .

• Technical Expertise:

• Data Modeling: OLTP, OLAP, dimensional, star schema, snowflake schema.
  
• Databases: SQL/NoSQL (Oracle, MySQL, PostgreSQL, MongoDB, Cassandra).
  
• Big Data: Hadoop, Spark, Kafka, Hive, Presto, Flink.
  
• Cloud: AWS (Redshift, S3, Glue), GCP (BigQuery, Dataflow), Azure (Synapse, Databricks).
  
• Data Integration: ETL/ELT pipelines using Airflow, Informatica, Talend, DBT.
  
• Data Warehousing & Lakes: Snowflake, Delta Lake, Databricks.
  
• APIs & Streaming: Real-time data processing with Kafka, Kinesis, Pub/Sub.
  

• Soft Skills:

  Strong stakeholder management, ability to work in fast-paced environments, leadership and mentoring skills, problem-solving mindset.

Preferred Experience in Retail/E-commerce:

• Customer 360 data platforms.
  
• Recommendation engines and personalization platforms.
  
• Inventory optimization and demand forecasting systems.
  
• Fraud detection and secure payment data management.
  
• Omnichannel retail analytics (online + offline).