- 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
Technology->Big Data - Data Processing->PySpark,Technology->Big Data - Data Processing->Spark->Apache Storm
