Senior Data Engineer - Data Platform & Warehouse

About the role

A company is looking for a Senior Data Engineer to build and scale the data foundations supporting its business. The role covers complex data warehouse architecture, pipeline development and migration, data quality and analytics enablement, and suits someone who enjoys solving large-scale data problems end-to-end.

Key responsibilities

• Design, build and evolve scalable data warehouse architecture and data models, including raw, staging, core/conformed and mart layers.
• Develop and maintain reliable batch and/or streaming data pipelines, ensuring scalability, performance and data quality.
• Drive migration and consolidation of legacy data pipelines and platforms, including validation, reconciliation, cutover and decommissioning.
• Establish consistent data definitions, metrics and modelling standards across business domains.
• Build data quality frameworks covering automated testing, freshness monitoring, anomaly detection and reconciliation.
• Partner with Analytics, BI, Product, Engineering and business teams to turn data requirements into scalable solutions and enable self-service analytics.
• Improve data discoverability and usability through certified datasets, documentation, semantic models and reusable data assets.
• Contribute to engineering best practices through architecture reviews, technical standards and mentoring of other Data Engineers.

Required profile

• Minimum 7+ years of Data Engineering experience building and operating large-scale data systems.
• Expert knowledge of dimensional modeling, star/snowflake schema and data vault.
• Proven ability to solve complex data problems and partner with Engineering, Analytics/BI, Product and business stakeholders.
• Preferred: data migration projects, cost-saving strategies for AWS Databricks, GCP BigQuery and Tableau; OTA, travel tech, e-commerce, marketplace or fintech experience; exposure to CDC, streaming, semantic/metrics layers or advanced modelling; experience leading technical designs, setting standards or mentoring engineers.

Required skills

• Hive, Spark, Flink, Kafka
• OLAP engines and lakehouse technologies such as ClickHouse, StarRocks, Greenplum, Iceberg or Hudi
• Advanced SQL, query performance tuning and ETL/ELT optimization
• Airflow and dbt for workflow orchestration and pipeline management