AI Data Engineer

ABOUT THE ROLE

We are looking for an experienced Senior Data Engineer to design and operate enterprise-grade data infrastructure — including warehouses, lakes, and marts — while ensuring data quality, governance, and compliance. You will bring deep expertise in data architecture and modeling, combined with proficiency in pipeline orchestration and analytics tooling.

KEY RESPONSIBILITIES
Data Architecture & Modeling

• Design enterprise data models using dimensional modeling, Data Vault 2.0, or similar methodologies

• Architect and maintain data warehouses, data lakes, and lakehouse environments

• Define and enforce data standards, naming conventions, and schema governance across domains

Data Governance & Quality

• Implement data governance frameworks including cataloging, lineage tracking, and metadata management

• Build automated data quality checks, validation rules, and anomaly detection into pipelines

• Ensure compliance with data privacy regulations through access controls and data classification

• Maintain master data management standards, data dictionaries, and business glossaries

Pipeline Engineering & Platform

• Build and maintain batch and streaming data pipelines with full observability and alerting

• Implement change data capture patterns, real-time ingestion, and ELT/ETL frameworks

• Administer and scale cloud data platforms; optimize storage, compute, and cost efficiency

• Manage data infrastructure using infrastructure-as-code practices

Collaboration

• Partner with analytics engineers, data scientists, and BI teams to deliver trusted data products

• Define data contracts between producers and consumers; mentor junior engineers

REQUIRED QUALIFICATIONS
• 5+ years of data engineering experience with a focus on enterprise data platforms

• Expert SQL skills; strong Python for data processing and automation

• Deep experience with cloud data warehouse platforms

• Hands-on experience with data transformation frameworks and workflow orchestration tools

• Experience with data governance and cataloging platforms

• Solid understanding of data quality frameworks, privacy regulations, and CI/CD for pipelines

NICE TO HAVE
AI & Machine Learning

• Experience building and maintaining feature stores to serve ML models in production

• Familiarity with vector databases and embedding pipelines for retrieval-augmented generation

• Exposure to LLM application frameworks and AI orchestration workflows

• Data lineage and audit trails applied to AI/ML workflows for compliance and reproducibility

Telecom & RAN

• Understanding of RAN architecture, including network nodes, interfaces, and data flows across 4G/5G environments

• Familiarity with telecom data sources such as performance counters, KPIs, alarm feeds, and network event logs

• Experience with high-volume, time-series network data and applying data engineering principles to telecom datasets

• Ability to collaborate with network engineers and RAN teams to define data requirements and support analytics use cases

Cloud Data Platforms

• Hands-on experience with Snowflake, including performance optimization, cost management, secure data sharing, and integration with modern ELT frameworks