Job Description
As a Data Engineer, you will help build the data foundation for our agentic AI platform. You will work with senior data architects, AI/ML engineers, and platform engineers to implement data ingestion, transformation, profiling, enrichment, validation, and preparation pipelines across structured and unstructured enterprise data sources.
This is a hands-on engineering role for someone who enjoys working with real-world enterprise data, building reliable pipelines, writing robust Python and SQL, and helping convert raw enterprise information into AI-ready data assets.
Key Responsibilities
- Build and maintain data ingestion pipelines for structured enterprise systems such as ERP, CRM, billing, finance, HR, OSS/BSS, ServiceNow, Salesforce, SAP, Oracle, databases, and APIs.
- Build pipelines for unstructured and semi-structured data sources such as documents, emails, logs, transcripts, PDFs, spreadsheets, and media metadata.
- Develop ETL/ELT workflows using Python, SQL, PySpark, Apache Spark, Airflow, dbt, Dagster, cloud-native services, or equivalent technologies.
- Support data profiling routines to identify missing values, duplicates, inconsistent formats, incomplete master data, schema changes, and conflicting records.
- Implement data quality checks using frameworks such as Outstanding Expectations, dbt tests, AWS Glue DataBrew, custom validation scripts, or equivalent tools.
- Support data labelling, contextualization, harmonization, enrichment, and classification workflows required for AI agent configuration.
- Prepare data outputs for downstream AI consumption, including embeddings, metadata, semantic tags, graph-ready datasets, and retrieval-ready document chunks.
- Work with vector databases and search indexes to support semantic retrieval and hybrid search use cases.
- Support knowledge graph and ontology implementation by preparing entities, relationships, attributes, mappings, and validation-ready datasets.
- Implement data masking, anonymizati
📌 Data Engineer (Noida)
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