Editorial summary

The company is seeking a Senior Data Engineer with over 8 years of IT experience, including at least 5 years in data engineering and building data pipelines. The role involves designing, developing, and maintaining production-grade data pipelines and lakehouse solutions in an on-premises environment, with a focus on performance, security, and best practices. Key requirements include expertise in SQL Server, Python, PySpark, Apache Spark, Kubernetes, Docker, and data modeling for relational and NoSQL databases. The position offers a salary of 12,000.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job id 32503 first appeared in Job-Q.com on 16 Sep 2026]

Job Summary

We are seeking a senior data engineer to design, build, and maintain production-grade data pipelines and lakehouse solutions in an on-premises environment, while driving data platform initiatives, performance, security, and technical best practices.

Mandatory Skill Set

• 8+ years of IT experience with 5+ years in data engineering/data pipelines;
• Expert SQL/SQL Server – query optimization, indexing & performance tuning;
• Advanced Python & PySpark;
• Apache Spark and distributed data processing;
• Kubernetes & Docker for on-premises deployments;
• Data modelling – relational & NoSQL; hands-on MongoDB/Cassandra;
• Lakehouse/Data Lake architecture, including Iceberg, partitioning & metadata;
• ETL/ELT orchestration – Apache Airflow or similar;
• CI/CD, Git, automated testing & IaC;
• Data security, governance, RBAC, masking & compliance.

Desired Skill Set

• Experience in data virtualization, logical data warehouses, Data Mesh/Fabric architectures, metadata management, and data lineage;
• Familiarity with real-time streaming technologies such as Kafka/Flink, along with experience in technical leadership, mentoring, and Agile data architecture. 

Responsibilities

• Design and develop production-grade ETL/ELT pipelines using Python/PySpark;
• Build and manage multi-layer lakehouse architecture across raw, curated, and consumption layers;
• Deploy scalable data pipelines using Kubernetes/Docker in on-premise environments;
• Develop data models and optimize complex SQL Server queries and performance;
• Establish CI/CD, automated testing, monitoring, and version-control practices;
• Implement data security, governance, and access controls;
• Mentor engineers, conduct code reviews, and drive engineering best practices;
• Collaborate with data scientists, BAs, and stakeholders to deliver business-ready datasets;
• Provide L3 support and lead resolution of complex data engineering challenges.

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