Lead BigData Engineer (Databricks + AWS)

In this role, you will lead the design and development of scalable data platforms on AWS, focusing on the Databricks ecosystem. You will guide a team of engineers, shape architectural decisions, and ensure high-quality delivery of batch and streaming data solutions. The position involves collaboration with business and technical stakeholders throughout the full project lifecycle, from discovery and design to production implementation.

• Proven experience as a Lead Data Engineer with a strong focus on data pipeline design
• Hands-on experience with both batch and streaming data processing
• Strong expertise in AWS cloud platform and Databricks (including Delta Lake, Unity Catalog, Workflows, and Jobs)
• Proficiency in Python (preferred), Scala, or Java, along with strong SQL skills
• Solid knowledge of big data technologies such as Apache Spark or Flink
• Experience with workflow orchestration tools such as Databricks Workflows, Apache Airflow, or MWAA
• Practical experience with streaming platforms such as Apache Kafka, Amazon MSK, or Kinesis
• Familiarity with data warehousing solutions such as Snowflake or Amazon Redshift
• Experience working with data formats and tools such as Avro, GitHub, and SQL-based systems
• Ability to translate business requirements into technical solutions and guide teams toward delivery
• Strong leadership and communication skills, with experience working with cross-functional stakeholders
• Upper-intermediate or higher level of English

• Design and implement scalable data solutions using Databricks on AWS, including Lakehouse architectures based on Delta Lake and Unity Catalog
• Lead the development of batch and streaming data pipelines using technologies such as Apache Spark or Flink
• Define and own source-to-target mappings, supporting data ingestion and integration from multiple sources
• Drive architecture decisions and contribute to scaling the data platform and data models
• Collaborate with stakeholders to gather requirements and translate them into technical solutions and implementation plans
• Guide and support a team of data engineers, defining scope, priorities, and best practices
• Develop and manage workflows using orchestration tools such as Databricks Workflows, Apache Airflow, or MWAA
• Work with streaming platforms such as Apache Kafka, Amazon MSK, or Kinesis for real-time data processing
• Leverage data storage and analytics solutions such as Snowflake or Amazon Redshift
• Ensure proper documentation of data models, schemas, and architecture decisions
• Participate in the full project lifecycle, from PoC and MVP to full-scale implementation
• Explore new technologies, build prototypes, and contribute to knowledge sharing within the engineering community