Senior Data Engineer

We are looking for a skilled Data Engineer with 8+ years of experience in big data technologies, particularly Python, PYSpark, SQL and data lakehouse architectures. The ideal candidate will have a strong background in building scalable data pipelines and experience with modern data storage formats, including Apache Iceberg. You will work closely with cross-functional teams to design and implement efficient data solutions in a cloud-based environment.

Key Responsibilities:

• Data Pipeline Development:

• Design, build, and optimize scalable data pipelines using Apache Spark.
• Implement and manage large-scale data processing solutions across data lakehouses.

• Data Lakehouse Management:

• Work with modern data lakehouse platforms (e.g.Apache Iceberg) to handle large datasets.
• Optimize data storage, partitioning, and versioning to ensure efficient access and querying.

• SQL & Data Management:

• Write complex SQL queries to extract, manipulate, and transform data.
• Develop performance-optimized queries for analytical and reporting purposes.

• Data Integration:

• Integrate various structured and unstructured data sources into the lakehouse environment.
• Work with stakeholders to define data needs and ensure data is available for downstream consumption.

• Data Governance and Quality:

• Implement data quality checks and ensure the reliability and accuracy of data.
• Contribute to metadata management and data cataloging efforts.

• Performance Tuning:

• Monitor and optimize the performance of Spark jobs, SQL queries, and overall data infrastructure.
• Work with cloud infrastructure teams to optimize costs and scale as needed.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.
• 8+ years of experience in data engineering, with a focus on Java/python, Spark and SQL Programming languages.
• Hands-on experience with Apache Iceberg, Snowflake, or similar technologies.
• Strong understanding of data lakehouse architectures and data warehousing principles.
• Proficiency in AWS data services.
• Experience with version control systems like Git and CI/CD pipelines.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.

Nice to Have:

• Experience with containerization (Docker, Kubernetes) and orchestration tools like Airflow.
• Certifications in AWS cloud technologies