Join our Team
About The Role
We are looking for an experienced Senior Data Engineer to design and operate enterprise-grade data infrastructure โ including warehouses, lakes, and marts โ 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
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