What you’ll do
- Design, build, and maintain scalable data pipelines across batch, micro-batch, and streaming use cases.
- Work with modern data platforms such as Databricks, Azure Data Lake, Delta Lake, Microsoft Fabric, Snowflake, or similar technologies.
- Develop ETL/ELT processes using Python, PySpark, SQL, Azure Data Factory, Databricks Workflows, Airflow, or similar orchestration tools.
- Implement medallion architecture patterns, including Bronze, Silver, and Gold data layers.
- Implement medallion architecture patterns, including Bronze, Silver, and Gold data layers.
- Build and optimize data models for analytics, reporting, AI, and machine learning use cases.
- Support data quality, logging, monitoring, auditability, and governance practices across data pipelines.
- Contribute to reusable engineering frameworks, metadata-driven pipelines, schema evolution logic, and standardized delivery practices.
- Collaborate with data architects, software engineers, AI engineers, analysts, and client stakeholders to turn business requirements into production-ready solutions.
- Support AI and GenAI initiatives by preparing reliable data foundations for embeddings, vector search, RAG pipelines, and intelligent applications.
What we are looking for
- 3+ years of hands-on experience in data engineering, software engineering, or a related technical role.
- Strong practical experience with Python, SQL, and PySpark.
- Experience designing and delivering ETL/ELT pipelines in cloud or lakehouse environments.
- Hands-on experience with Databricks, Spark, Delta Lake, Azure Data Factory, Azure Data Lake, Microsoft Fabric, Snowflake, or similar platforms.
- Good understanding of data warehousing and data modeling concepts, including dimensional modeling, SCD Type 1/2, CDC, and medallion architecture.
- Experience working with Git, CI/CD practices, automated testing, and modular code design.
- Understanding of data quality, schema evolution, logging, monitoring, and production support.
- Ability to communicate clearly with technical and non-technical stakeholders.
- Curiosity about AI, machine learning, and generative AI, with a willingness to apply these technologies in client-facing solutions.
Nice to have
- Experience with Azure, AWS, or GCP cloud services.
- Experience with Kafka, Event Hub, Spark Structured Streaming, or similar streaming technologies.
- Experience with dbt, Airflow, Unity Catalog, Purview, Great Expectations, or other DataOps and governance tools.
- Exposure to machine learning, NLP, LLMs, vector databases, RAG, LangChain, CrewAI, Semantic Kernel, or similar AI frameworks.
- Experience working in consulting, client delivery, or international project environments.
- Cloud, Databricks, Microsoft Fabric, Snowflake, or AI-related certifications.
What we offer
- Opportunity to work on international data and AI projects with real business impact.
- Hands-on experience with modern cloud, data, and AI technologies.
- A collaborative engineering culture focused on learning, ownership, and high-quality delivery.
- Support for professional development, certifications, conferences, and continuous learning.
- Hybrid/remote work model and a flexible, growth-oriented environment.
- Private health insurance and competitive benefits.