Senior Data Engineer

Senior Data Engineer at BlueChip Technologies

Description

• As part of our continued growth, we are seeking an experienced Senior Data Engineer to join our Data Services Division.
• This is a permanent, client-embedded role supporting tier-1 telecom customers, where you will build, operate, and continuously enhance large-scale Apache Open-Source Lakehouse and analytics platforms.
• The successful candidate will play a critical role in ensuring platform reliability, delivering new analytics use cases, and serving as a trusted technical partner to our client.

Key Responsibilities

• Design, develop, test, and maintain large-scale data pipelines using Apache Kafka, Spark/PySpark, Flink, Airflow, Trino, and Apache Iceberg/Delta Lake.

• Build and support Bronze, Silver, and Gold medallion-layer data pipelines processing high-volume telecom data at terabyte and petabyte scale.

• Design and implement dimensional (Kimball-aligned) data models, including fact tables, dimension tables, star schemas, and slowly changing dimensions (SCDs).

• Contribute to data platform architecture by defining partitioning strategies, schema evolution, and storage optimization.

• Serve as the primary technical point of contact for client data engineering requirements, participating in client meetings and resolving operational issues within agreed service levels.

• Translate business and analytics requirements into scalable data pipelines and reusable data products.

• Monitor pipeline performance, investigate and resolve data quality issues, and implement monitoring, alerting, and observability for production environments.

• Maintain architecture documentation, runbooks, data dictionaries, and technical documentation to ensure knowledge continuity and reduce key-person dependency.

• Mentor junior and mid-level Data Engineers through code reviews, technical guidance, and knowledge-sharing initiatives.

• Collaborate with Data Architects, BI Developers, Data Analysts, and Project Managers to deliver high-quality data solutions aligned with business objectives and project timelines.

• Qualifications & Requirements

• Bachelor’s Degree in Computer Science, Engineering, Information Technology, or a related discipline (or equivalent practical experience).

• Minimum of 6 years’ professional experience in Data Engineering, including at least 3 years supporting large-scale (terabyte/petabyte) data platforms.

• Proven hands-on experience with the Apache Open-Source ecosystem, including Apache Spark (PySpark), Apache Kafka, Apache Airflow, and Apache Iceberg or Delta Lake.

• Strong SQL expertise and practical experience designing and implementing dimensional (Kimball) data models.

• Experience building and supporting Medallion Architecture (Bronze, Silver, Gold) Lakehouse solutions with robust data quality and reconciliation processes.

• Strong Python programming skills for data engineering, automation, testing, and pipeline development.

• Experience working within Linux and cloud-based environments.

• Experience with telecom data sources such as Call Detail Records (CDRs), OSS/BSS systems, network probe data, or similar large-scale datasets is highly desirable.

• Experience with Huawei network probe data ingestion will be considered a significant advantage.

• Familiarity with workflow orchestration, CI/CD pipelines, and Infrastructure-as-Code practices.

Key Competencies:

• Strong client engagement and stakeholder management skills.
• Excellent written and verbal communication abilities.
• Demonstrated ownership mindset with strong documentation practices.
• Ability to manage multiple priorities across operational support and new solution delivery.
• Strong analytical, troubleshooting, and problem-solving skills.
• Collaborative team player with experience mentoring junior engineers.
• Reliable, dependable, and comfortable working within a client-embedded hybrid environment.

What We Offer

• Competitive, performance-driven salary package.
• Comprehensive health insurance and employee benefits.
• Pension and retirement savings plan.
• Continuous learning, technical certifications, and professional development opportunities.
• Exposure to enterprise-scale data engineering projects and emerging technologies.
• A collaborative, innovative, and inclusive work environment.
• Hybrid work model with modern tools and technologies.

Application Closing Date

Not Specified.