Lead Big Data Engineer

TL;DR

Lead Big Data Engineer (Python/GCP/Databricks): Building scalable batch and streaming data pipelines and cloud-native data platforms with an accent on data architecture, processing reliability, and analytics engineering. Focus on designing Lakehouse solutions, optimizing large-scale workflows, and delivering production systems across the full project lifecycle.

Location: Remote or office-based in Bulgaria, Poland, or Romania

Company

hirify.global delivers engineering and technology services, including cloud, data, and analytics solutions.

What you will do

• Design, develop, and maintain scalable batch and streaming data pipelines.
• Build data processing solutions with Python, SQL, Java, Apache Spark, and Databricks.
• Create cloud-native architectures using GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and Cloud Storage.
• Implement data transformation, modeling, and analytics engineering practices with dbt and Dataform.
• Collaborate with stakeholders, architects, and engineering teams to translate data requirements into technical solutions.
• Contribute to architecture decisions, data quality, reliability, performance, PoCs, MVPs, and production deployments.

Requirements

• 5+ years of professional experience in Big Data or Data Engineering.
• Advanced Python and SQL skills for large-scale data processing; Java is optional.
• Hands-on experience with GCP, Apache Spark, Cloud Dataflow or Apache Beam, and Databricks Lakehouse concepts.
• Experience with orchestration tools such as Apache Airflow or Cloud Composer and streaming technologies such as Apache Kafka or Google Cloud Pub/Sub.
• Strong knowledge of BigQuery, cloud data architectures, dbt, and Dataform.
• Upper-intermediate English or higher, with strong analytical, troubleshooting, problem-solving, and communication skills.

Culture & Benefits

• Remote or office-based work options.
• Collaborative engineering environment involving technical and non-technical stakeholders.
• Opportunity to work across discovery, solution design, implementation, deployment, and platform enhancement.