Senior Data Engineer
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Job Description
Senior Data Engineer building cloud data platforms for Valtech, an experience innovation company transforming brands through data, AI, creativity, and technology. Enabling analytics, GenAI, and production-grade data products.
Responsibilities:
- 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
Requirements:
- 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
Benefits:
- 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
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