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.
Should you be interested in this career opportunity, please send in your updated resume to ************* at the earliest.
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