Data Architect

We are seeking an experienced Data Architect to help lead the design and implementation of next generation data platforms leveraging Object Storage and Apache Iceberg. This role will be responsible for architecting and executing large scale data migrations from traditional relational and analytical databases into modern lakehouse environments built on object storage and Iceberg catalogs.

The ideal candidate will have hands on experience designing enterprise data architectures, migrating data from platforms such as Hadoop, Teradata, Oracle, and other database technologies into object storage platforms. The candidate must possess deep expertise in Apache Iceberg, metadata management, data modeling, ingestion frameworks, and large-scale migration strategies.

Key Responsibilities

• Lead the architecture, design, and implementation of enterprise data platforms utilizing Object Storage and Apache Iceberg.
• Develop migration strategies and execution plans for moving data from relational databases, data warehouses, and Hadoop ecosystems into Iceberg based architectures.
• Design scalable ingestion frameworks to support batch, streaming, and near real-time data loads into object storage.
• Have deep understanding of Iceberg catalog structures, namespaces, partitioning strategies, schema evolution, and data lifecycle management.
• Establish best practices for security, metadata management, retention, and regulatory compliance.
• Collaborate with application teams, data engineers, DBAs, and platform teams to assess migration readiness and modernize legacy workloads.
• Optimize data models supporting Bronze, Silver, and Gold data layers within a lakehouse architecture.
• Evaluate and implement solutions for data access, query performance, and interoperability across compute engines such as Spark, Trino, Databricks, Snowflake, Teradata, or similar technologies.
• Guide the conversion of ETL workloads and legacy data integration processes into modern data engineering frameworks.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• 5+ years of experience in Data Architecture, Data Engineering, or Data Platform modernization.
• 2+ years of hands-on experience implementing Apache Iceberg in enterprise environments.
• Demonstrated experience migrating large scale datasets from traditional databases and data warehouses into object storage and lakehouse architectures.
• Strong understanding of Object Storage technologies (S3-compatible platforms, Pure storage etc.).
• Experience implementing and managing Iceberg catalogs
• Expertise in data modeling, partitioning strategies, schema evolution, metadata management, and performance optimization.
• Experience designing and implementing enterprise ETL architectures.
• Strong understanding of data governance, security, lineage, and compliance requirements.
• Understanding/Experience with Postgres database

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field.
• 5+ years of experience in Data Architecture, Data Engineering, or Data Platform modernization.
• 2+ years of hands-on experience implementing Apache Iceberg in enterprise environments.

Key Responsibilities

• Lead the architecture, design, and implementation of enterprise data platforms utilizing Object Storage and Apache Iceberg.
• Develop migration strategies and execution plans for moving data from relational databases, data warehouses, and Hadoop ecosystems into Iceberg based architectures.
• Design scalable ingestion frameworks to support batch, streaming, and near real-time data loads into object storage.
• Have deep understanding of Iceberg catalog structures, namespaces, partitioning strategies, schema evolution, and data lifecycle management.
• Establish best practices for security, metadata management, retention, and regulatory compliance.
• Collaborate with application teams, data engineers, DBAs, and platform teams to assess migration readiness and modernize legacy workloads.
• Optimize data models supporting Bronze, Silver, and Gold data layers within a lakehouse architecture.
• Evaluate and implement solutions for data access, query performance, and interoperability across compute engines such as Spark, Trino, Databricks, Snowflake, Teradata, or similar technologies.
• Guide the conversion of ETL workloads and legacy data integration processes into modern data engineering frameworks.