Data Architect
Lorven Technologies Inc.
2 - 5 years
Bhubaneswar
Posted: 27/04/2026
Job Description
Job Title: Data Architect
Location: [Specify Location / Remote / Hybrid]
Experience: 815 Years
Employment Type: Full-Time
Job Summary
We are looking for an experienced Data Architect to design, build, and manage scalable, secure, and high-performance data systems. The ideal candidate will define enterprise data architecture, ensure data quality and governance, and enable advanced analytics and AI use cases.
Key Responsibilities
- Design and implement enterprise data architecture (data lakes, data warehouses, and data marts)
- Define data models (conceptual, logical, and physical) aligned with business needs
- Build scalable data pipelines for batch and real-time processing
- Establish and enforce data governance, data quality, and security standards
- Architect solutions for big data platforms and distributed systems
- Collaborate with business, analytics, and engineering teams to define data strategy
- Ensure data integration across multiple sources (structured & unstructured)
- Optimize performance, scalability, and cost of data platforms
- Enable advanced analytics, BI, and AI/ML workloads
- Provide technical leadership and mentorship to data engineering teams
Mandatory Skills
- Strong experience in data architecture and data modeling
- Expertise in SQL and database design (RDBMS & NoSQL)
- Hands-on experience with data warehousing solutions (Snowflake, Redshift, BigQuery, etc.)
- Experience with big data technologies (Spark, Hadoop, Kafka)
- Proficiency in ETL/ELT tools and data pipeline design
- Strong knowledge of data governance, metadata management, and data quality frameworks
- Experience with cloud platforms (AWS / Azure / GCP)
- Programming knowledge in Python / Scala / Java
Preferred Skills
- Experience with Lakehouse architecture (Delta Lake, Iceberg, Hudi)
- Knowledge of Data Mesh / Data Fabric architecture
- Exposure to AI/ML data pipelines and feature engineering
- Familiarity with data visualization tools (Power BI, Tableau)
- Understanding of DevOps / MLOps practices
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