AI Data Engineer

Remopt

About The Position

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. The role calls for deep expertise in data architecture and modeling, along with proficiency in pipeline orchestration and analytics tooling. The position covers data architecture and modeling, governance and quality, and pipeline engineering and platform operations. It also involves partnering with analytics engineers, data scientists, and BI teams to deliver trusted data products, defining data contracts between producers and consumers, and mentoring junior engineers.

Requirements

5+ years of data engineering experience focused on enterprise data platforms.
Expert SQL skills and strong Python skills 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 Haves

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.
Experience applying data lineage and audit trails to AI/ML workflows for compliance and reproducibility.
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.
Hands-on experience with Snowflake, including performance optimization, cost management, secure data sharing, and integration with modern ELT frameworks.

Responsibilities

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.
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.
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.
Partner with analytics engineers, data scientists, and BI teams to deliver trusted data products.
Define data contracts between producers and consumers.
Mentor junior engineers.