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.

• 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 Great 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.

• Working knowledge of data pipeline development using PySpark, Apache Spark, Airflow, dbt, Dagster, or equivalent technologies.

• Experience working with structured data from databases, APIs, enterprise applications, data lakes, warehouses, or lakehouse platforms.

• Exposure to cloud data platforms such as Databricks, Snowflake, BigQuery, Azure Data Lake, AWS S3, Google Cloud Storage, or equivalent platforms.

• Understanding of data modelling, schema design, joins, keys, relationships, data validation, and data quality concepts.

• Practical experience with data profiling, cleansing, transformation, and reconciliation.

• Familiarity with Git, CI/CD basics, unit testing, and production-grade engineering practices.