Senior Data Engineer

• Design, build, and optimize modern cloud-based data platforms powering analytics, AI, and data products
• Develop reliable batch, streaming, and near-real-time pipelines for structured and unstructured data
• Build ingestion, transformation, curation, and data-serving workflows
• Implement lakehouse architectures and medallion layering in Databricks or Fabric
• Deliver curated datasets for analytics, machine learning, causal modeling, optimization, GenAI, RAG, and agent-based systems
• Use MLflow, Unity Catalog, Azure ML, and equivalent platform tooling to support production-grade AI systems
• Design data models and orchestrate workflows using Airflow, dbt, Databricks Workflows, Azure Data Factory, or equivalents
• Apply data governance, lineage, quality, access control, and observability practices
• Monitor data freshness, pipeline reliability, SLA adherence, performance, scalability, and cost efficiency
• Enable APIs, feature inputs, and analytical endpoints for downstream ML and AI systems
• Discover and refine business requirements through stakeholder engagement and analysis
• Collaborate with data scientists, ML engineers, analysts, platform teams, and business stakeholders
• Support adoption of data products and contribute to data and AI best practices

• Strong hands-on experience with Apache Spark and Delta Lake
• Strong programming skills in Python and SQL
• Proven experience building batch and streaming data pipelines and production-grade data platforms
• Solid understanding of data modeling, data quality, and governance principles
• Strong, demonstrable hands-on experience with either AWS data services and the Databricks Lakehouse Platform, or Microsoft Azure/Fabric and related tooling
• Experience with lakehouse architectures and distributed data systems
• Strong understanding of scalability, reliability, and performance considerations in data pipelines
• Naturally curious, with strong problem-solving skills
• Collaborative approach to working in cross-functional teams
• Experience in Agile or consulting environments is beneficial
• Familiarity with Snowflake or GCP is a plus but not required
• Deep expertise in both AWS and Azure/Fabric, GenAI and AI data systems, CI/CD for data pipelines, and infrastructure-as-code tools are nice to have but not required
• Additional exposure to Kafka, Spark optimization, advanced analytics and ML workloads, or large-scale analytics platforms is valuable

• Flexibility, with remote and hybrid work options (country-dependent)
• Career advancement, with international mobility and professional development programs
• Learning and development, with access to cutting-edge tools, training and industry experts
• Competitive compensation package