Lead Software Engineer

As a Data Engineer, you will design, build, and support high-quality data solutions and pipelines using modern engineering practices and tools.
Design, develop, and enhance scalable data pipelines and data processing systems.
Build reusable ingestion, transformation, and storage patterns to support product and analytical needs.
Integrate data from diverse sources including APIs, databases, event streams, and third‑party systems.
Implement data models and schemas that create unified, consistent views of business operations.
Apply modern engineering practices, including version control, testing, CI/CD, and automated deployments.
Ensure data quality, integrity, and reliability through validation, monitoring, and observability tools.
Optimize data workflows for performance, cost efficiency, and operational resilience.
Document data flows, lineage, and technical components in support of transparency and maintainability.
Governance, Standards & Continuous Improvement
Contribute to data governance practices such as metadata management and data lineage.
Ensure compliance with data privacy, security, and regulatory requirements.
Evaluate new technologies, frameworks, and patterns to improve data infrastructure and engineering capabilities.
Skills
Strong proficiency with data pipeline development in Python, Java, or Scala.
Modern data frameworks (Spark, Kafka, Flink, dbt, or equivalent).
Solid understanding of SQL and NoSQL databases and data modeling principles.
Ability to optimize SQL, pipelines and storage for performance and cost.
Experience building batch and/or streaming data solutions and using Microsoft Fabric Notebooks to develop, debug, and operationalize data engineering workflows.
Experience with containerization and orchestration tools (Docker, Kubernetes) , observability and monitoring tools (Datadog preferred) ,cloud data services (Azure, AWS).
Hands‑on with Microsoft Fabric components, Lakehouse, Warehouse, Data Pipelines, Dataflows Gen2, and Semantic Models.
Familiarity with CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps, etc) & generative and agentic AI tools to improve engineering productivity
Performance tuning for data pipelines, databases, and queries.

Work arrangement
No