We are seeking an experienced Senior Data Engineer to design, build, and manage scalable, reliable, and high-performance data systems. The ideal candidate will have strong expertise in modern data engineering tools, cloud technologies, and end-to-end pipeline development. This role requires a hands-on technical leader capable of driving data initiatives, collaborating across teams, and ensuring efficient data delivery and analytics readiness.
Key Responsibilities:
- Design, develop, and maintain scalable data architectures and ETL/ELT pipelines.
- Build and orchestrate workflows using Apache Airflow and similar orchestration tools.
- Implement data ingestion from multiple sources, including REST APIs, GraphQL endpoints, and SFTP servers.
- Work extensively with big data and stream-processing frameworks such as Apache Spark and Kafka.
- Manage and optimize cloud-based infrastructure, with strong experience in AWS (EC2, RDS, S3).
- Use Databricks for large-scale data processing, analytics, and collaboration.
- Leverage SQLMesh for modern, reliable data workflow management.
- Ensure data quality, performance, and governance standards across systems.
- Collaborate with analytics, product, and engineering teams to support business intelligence and data-driven decision-making.
Required Skills & Experience:
- Minimum 6 years of experience in Data Engineering or similar technical roles.
- Strong proficiency in Spark, Kafka, and Databricks.
- Deep understanding of SQL and data modeling principles.
- Proven experience with AWS Cloud Services (EC2, RDS, S3, Lambda, etc.).
- Expertise in Airflow for pipeline orchestration and scheduling.
- Experience integrating external systems via APIs and SFTP.
- Strong knowledge of SQLMesh and modern data architecture frameworks.
- Excellent communication, collaboration, and leadership skills.
Preferred Attributes:
- Experience working in agile data-driven environments.
- Strong analytical mindset with attention to scalability, performance, and reliability.
- Passion for automation, innovation, and continuous improvement in data engineering.