About the position
We are looking for a highly skilled Data Architect to design and build modern data architecture foundations for our customers. This role involves supporting real-time, high-scale (Big Data) data pipelines and ML/AI use cases, including Generative AI. The ideal candidate will have extensive experience in data engineering, data architecture, and cloud-native environments.
Responsibilities
- Design and build modern data architecture foundations for our customers, supporting real-time, high-scale (Big Data) data pipelines and ML/AI use cases, including Generative AI.
- Map customers' data needs and lead the selection and implementation of key technologies across the stack: data lakes (e.g., Iceberg), databases, ETL/ELT tools, orchestrators, data quality and observability frameworks, and statistical/ML tools.
- Design and build a cloud-native, cost-efficient, and scalable data infrastructure from scratch, capable of supporting rapid growth, high concurrency, and low-latency SLAs (e.g., 1-second delivery).
- Lead design reviews and provide architectural guidance for all data solutions, including data engineering, analytics, and ML/data science workflows.
- Set high standards for data quality, integrity, and observability. Design and implement processes and tools to monitor and proactively address issues like missing events, data delays, or integrity failures.
- Collaborate cross-functionally with internal and customer teams to ensure alignment between infrastructure, customer goals, and real-world constraints.
- Mentor engineers and promote best practices around data modeling, storage, streaming, and observability.
- Stay up-to-date with industry trends, evaluate emerging data technologies, and lead POCs to assess new tools and frameworks — especially in the domains of Big Data architecture, ML infrastructure, and Generative AI platforms.
Requirements
- At least 10 years of experience in a data engineering role, including 2+ years as a data architect with ownership over company-wide architecture decisions.
- Proven experience designing and implementing large-scale, Big Data infrastructure from scratch in a cloud-native environment (GCP preferred).
- Excellent proficiency in data modeling, including conceptual, logical, and physical modeling for both analytical and real-time use cases.
- Strong hands-on experience with integration tools (e.g., Boomi).
- Data lake and/or warehouse technologies, with Apache Iceberg experience required (e.g., Iceberg, Delta Lake, BigQuery, ClickHouse).
- ETL/ELT frameworks and orchestrators (e.g., Airflow, dbt, Dagster).
- Real-time streaming technologies (e.g., Kafka, Pub/Sub).
- Data observability and quality monitoring solutions.
- Excellent proficiency in SQL, and in either Python or Spark.
- Experience designing efficient data extraction and ingestion processes from multiple sources and handling large-scale, high-volume datasets.
- Demonstrated ability to build and maintain infrastructure optimized for performance, uptime, and cost, with awareness of AI/ML infrastructure requirements.
- Experience working with ML pipelines and AI-enabled data workflows, including support for Generative AI initiatives (e.g., content generation, vector search, model training pipelines) — or strong motivation to learn and lead in this space.
- Excellent communication skills in English, with the ability to clearly document and explain architectural decisions to technical and non-technical audiences.
- Fast learner with strong multitasking abilities; capable of managing several cross-functional initiatives simultaneously.
Nice-to-haves
- Experience leading POCs and tool selection processes.
- Familiarity with Databricks, LLM pipelines, or vector databases is a strong plus.
Benefits
- Opportunity to work on cutting-edge data integration projects.
- Collaborative and inclusive work environment.
- Competitive salary and benefits package.
- Professional growth and development opportunities.
