SoftServe delivers engineering and technology services for enterprise clients.
What you will do:
- Design, build, and optimize scalable AWS data solutions using Databricks, including Lakehouse architectures with Delta Lake and Unity Catalog.
- Develop batch and real-time data processing using Apache Spark, Flink, Kafka, Amazon MSK, and Kinesis.
- Lead data integration and migration (source-to-target mapping, ingestion, transformation, and data quality assurance).
- Drive data platform architecture, data modeling, and engineering best practices for scalability, reliability, and long-term maintainability.
- Build and manage automated workflows and data pipelines using Databricks Workflows, Apache Airflow, MWAA, Snowflake, and Amazon Redshift.
- Mentor data engineering teams and translate business requirements into technical solutions, roadmaps, and delivery plans.
Requirements:
- Proven experience as a Lead Data Engineer delivering scalable data platforms and pipelines.
- Hands-on expertise in batch and real-time processing with Apache Spark, Flink, Kafka, Amazon MSK, or Kinesis.
- Strong AWS and Databricks experience, including Delta Lake, Unity Catalog, Workflows, and Jobs.
- Advanced SQL plus proficiency in Python (preferred), Scala, or Java.
- Experience orchestrating data workflows with Databricks Workflows, Apache Airflow, or MWAA.
- Upper-intermediate or higher English.
Culture & Benefits:
- Remote/office work model.
- Collaborative, innovation-driven environment across the full solution lifecycle.
- Opportunity to explore emerging technologies and share knowledge within the engineering community.
- Leadership role with mentoring and team enablement.
Hiring process: Submit the application form; recruiter follow-up and interview steps.