AI Data Architect / Senior Data Engineer - DaAI

Educational Requirements Bachelor of Engineering

Service Line Global Delivery

Responsibilities

• Design production-grade enterprise connectors and ETL/ELT pipelines for both structured enterprise systems such as ERP, CRM, OSS/BSS, billing, finance, HR, and unstructured sources such as emails, documents, logs, transcripts, and media files.
• Build ingestion and transformation pipelines using Python, SQL, PySpark, Apache Spark, Airflow, dbt, Dagster, Flink, or equivalent technologies.
• Create frameworks for data labelling, contextualization, harmonization, enrichment, and classification workflows to configure AI agents.
• Architect integration with knowledge graphs and vector databases for hybrid search, semantic retrieval, contextual reasoning, and AI-ready data access.
• Build and maintain Ontology/knowledge graph pipelines using Neo4j, RDF/OWL, Apache Jena, Stardog, GraphDB, or equivalent technologies.
• Implement graph validation frameworks such as SHACL or ShEx to programmatically enforce data integrity rules over enterprise knowledge graphs.
• Implement data quality automation using frameworks such as Great Expectations, AWS Glue DataBrew, dbt tests, custom validation pipelines, or equivalent tools.

Additional Responsibilities:

• Exposure to telecom, BFSI, manufacturing, or other complex enterprise domains.
• Experience with OSS/BSS, ERP, CRM, billing, order management, product catalog, service inventory, or network inventory systems.
• Experience with RDF triple stores such as Apache Jena, Stardog, GraphDB, Amazon Neptune, or equivalent technologies.
• Experience with data catalogues, metadata management tools, lineage platforms, or governance platforms.

Technical and Professional Requirements:

• Strong production experience on modern data platforms such as Databricks, Snowflake, BigQuery, cloud data lakes, lakehouses, or equivalent enterprise data platforms.
• Deep working knowledge of Python, SQL, PySpark, Apache Spark, and modern data pipeline development practices.
• Hands-on experience with both structured and unstructured data ingestion at enterprise scale.
• Strong experience in building pipelines for enterprise sources such as ERP, CRM, OSS/BSS, billing systems, finance systems, ServiceNow, Salesforce, SAP, Oracle, and legacy databases.
• Working knowledge of vector databases such as Pinecone, Weaviate, pgvector, Milvus, Chroma, or equivalent technologies.
• Hands-on knowledge of knowledge graphs, graph data modelling, graph querying, and enterprise graph implementation using Neo4j, Cypher, RDF, OWL, or equivalent technologies.

Preferred Skills:

• Technology->Data Management - Data Integration->Informatica->Informatica - Data Explorer
• Technology->Data Management->Data Architecture->Data Architecture - Data Management
• Technology->Microsoft Technologies->Microsoft Technologies- ALL
• Technology->Data on Cloud-DataStore->Snowflake
• Technology->AI-Data science->Databricks Machine Learning