job specification

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Responsibilities:

• Design and implement scalable data pipelines using Databricks and Kafka
• Build and maintain real-time streaming solutions for high-volume data
• Collaborate with cross-functional teams to integrate data flows into broader systems
• Optimize performance and reliability of data processing workflows
• Ensure data quality, lineage, and compliance across streaming and batch pipelines
• Participate in agile development processes and contribute to technical documentation

Mandatory Skills Description:

• +5 years of experience in data engineering roles
• Proven expertise with Databricks (Spark, Delta Lake, notebooks, performance tuning)
• Strong hands-on experience with Apache Kafka (topics, producers/consumers, schema registry)
• Solid understanding of streaming frameworks (e.g., Spark Structured Streaming, Flink, or similar)
• Experience with cloud platforms (AWS, Azure, or GCP)
• Proficiency in Python or Scala for data pipeline development
• Familiarity with CI/CD pipelines (GitLab, Jenkins) and agile tools (Jira)
• Exposure to data lakehouse architectures and best practices
• Knowledge of data governance, security, and observability

Project Description:

• We are seeking a skilled and hands-on Data Engineer with proven experience in Databricks, Apache Kafka, and real-time data streaming solutions.