Principal Data Engineer

Systems Limited; Punjab; On-site; PKR 3,200,000 - 5,200,000; Full time

Job summary

Systems Limited is seeking a Principal Data Engineer with 8+ years of hands-on experience in real-time and batch data processing. You will design and maintain scalable pipelines, containerize applications with Docker, and orchestrate workflows using Airflow or Azure Data Factory.

Experience with Spark (PySpark), Kafka, Flink, and lakehouse storage is essential. Leadership and agile collaboration are required to drive robust data solutions.

Qualifications

• 8+ years of hands-on data engineering experience with real-time and batch pipelines.
• Strong Python coding practices with modular, testable code.
• Experience with at least one cloud platform (Azure preferred).
• Real-time data processing using Kafka, Flink or Spark Streaming.
• Spark (PySpark) expertise for batch processing.
• Lakehouse architectures and columnar storage (Parquet/Delta/Iceberg).
• Orchestration using Airflow or Azure Data Factory.
• APIs to expose large datasets; data quality tooling (Great Expectations).
• Docker, Kubernetes and Helm for deployments; Terraform for infra.
• Agile mindset; mentoring data engineers; collaboration with DevOps.

Responsibilities

• Design, build, and maintain real-time data pipelines using Kafka, Flink, or Spark Streaming.
• Develop batch workflows with Apache Spark (PySpark).
• Orchestrate data pipelines with Airflow or Azure Data Factory.
• Containerize apps with Docker; deploy with Helm on Kubernetes.
• Use Lakehouse formats (Parquet/Delta/Iceberg) for storage.
• Build high-performance analytics engines with Trino or Presto.
• Collaborate with DevOps to manage infra with Terraform and CI/CD via Azure DevOps.
• Ensure data quality with Great Expectations; write modular Python/SQL.
• Develop observability to monitor pipeline performance; optimize throughput.
• Lead and mentor a team of data engineers; participate in agile ceremonies.

Education

Bachelor's degree in computer science or related discipline

Tools

Docker

Kubernetes

Helm

Apache Airflow

Azure Data Factory

Terraform

Azure DevOps

Trino/Presto

Spark (PySpark)

Kafka

Flink

Delta Lake

Parquet

Iceberg

ClickHouse

Job description

We are seeking a Principal Data Engineer with 8+ years of hands-on experience and a strong background in real-time and batch data processing, containerization, and cloud-based data orchestration. This role is ideal for someone passionate about building robust, scalable, and efficient data pipelines, and who thrives in agile, collaborative environments.

Key Responsibilities

• Design, build, and maintain real-time data pipelines using streaming frameworks such as Kafka, Apache Flink, and Spark Structured Streaming.
• Develop batch processing workflows with Apache Spark (PySpark)
• Orchestrate and schedule data workflows using orchestration frameworks such as Apache Airflow and Azure Data Factory
• Containerize applications using Docker, manage deployments with Helm, and run them on Kubernetes
• Implement modern storage solutions using open formats such as Parquet, Delta Lake, and Apache Iceberg
• Build high-performance analytics engines using tools like Trino or Presto
• Collaborate with DevOps to manage infrastructure with Terraform and integrate with CI/CD pipelines via Azure DevOps
• Ensure data quality and consistency using tools like Great Expectations
• Write modular, well-tested, and maintainable Python and SQL code
• Develop an observability layer to monitor and optimize performance across data pipelines
• Lead and mentor a team of data engineers
• Participate in agile ceremonies and contribute to sprint planning and reviews

Required Skills & Experience

• Advanced Python programming with a strong focus on modular and testable code
• Strong knowledge of SQL and experience working with large-scale datasets
• Hands-on experience with at least one major cloud platform (Azure preferred)
• Solid experience with real-time data processing (Kafka, Flink, or Spark Streaming)
• Expertise in Apache Spark (PySpark) for batch processing
• Experience implementing lakehouse architectures and working with columnar storage (e.g., ClickHouse)
• Proficient in using Azure Data Factory or Apache Airflow for data orchestration
• Experience in building APIs to expose large datasets
• Solid experience with Docker, Kubernetes, and Helm
• Familiarity with data lake open formats such as Parquet, Delta Lake, and Iceberg
• Basic experience with Terraform for infrastructure provisioning
• Practical experience with data quality frameworks (e.g., Great Expectations)
• Comfortable working in agile development teams
• Proven ability in debugging and performance tuning of streaming and batch data jobs
• Experience with AI-driven tools (e.g., text-to-SQL) is a plus
• Bachelor’s degree in computer science or related discipline