Job Description

Roles & Responsibilities

Build and operate data pipelines for AI use cases including ETL/ELT from OpCo BSS/OSS and enterprise systems

Create and maintain feature stores for ML models ensuring data quality, lineage, and governance

Coordinate data access with OpCo IT teams, Group Data CoE, and data governance functions

Assess data quality, completeness, and fitness-for-purpose for AI initiatives and drive remediation

Implement data governance controls for AI including privacy, consent, retention, and audit requirements

Support GenAI/RAG engineers with data preparation, ingestion, and optimization for vector databases

Design and implement data architectures for real-time and batch AI workloads

Monitor data pipeline health, performance, and SLAs with proactive issue resolution

Document data assets, schemas, and lineage in data catalog for discoverability and reuse

Partner with OpCo data engineers on data quality improvements and standardization initiatives

KPIs can include but are not limited to:

• Data availability: percentage of required data sources accessible and pipeline-ready for AI use cases
• Data quality: data quality scores (completeness, accuracy, timeliness) meeting AI requirements
• Pipeline reliability: uptime and performance of data pipelines meeting defined SLAs
• Time-to-data: average time from data requirement to production-ready data pipeline
• Stakeholder satisfaction: OpCo and AI team satisfaction with data engineering support

Desired Candidate Profile

Educational Qualification: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related technical field.

Experience: 5-8 years in data engineering with 2+ years focused on AI/ML data pipelines. Hands-on experience with feature stores, data quality, and large-scale data processing. Experience in telecom domain with BSS/OSS data systems highly valuable.

Knowledge: Strong expertise in data engineering tools and platforms (Spark, Airflow, Kafka, cloud data platforms). Experience with feature stores, data quality frameworks, and ML data pipelines. Understanding of data governance, privacy, and compliance. SQL and Python proficiency. Knowledge of vector databases and embeddings for GenAI advantageous.