Senior Data Engineer

The employer is a large organisation operating within the professional services industry, known for delivering innovative solutions to its clients. It is committed to fostering a culture of excellence and providing its employees with opportunities to grow and thrive in their careers.

• 5+ years of experience in Data Engineering in production-scale environments
• Very strong SQL skills and experience with analytical / columnar databases
• Strong Python skills, especially for data processing (pandas, numpy, pyarrow)
• Experience building ETL pipelines and data orchestration workflows (Airflow, Prefect, dbt)
• Solid understanding of OLAP systems, data warehousing, and performance optimization
• Experience with multi-tenant SaaS data platforms and data isolation concepts
• Knowledge of Row-Level Security (RLS), RBAC, and secure data access patterns
• Familiarity with BI tools, embedded analytics, and dashboarding systems
• Understanding of data compliance requirements (SOC2, GDPR)

Nice to have:

• Experience with scalable, real-time data processing systems
• Experience with distributed or streaming systems (Kafka, Pulsar, or Kinesis)

• Design and build scalable ETL pipelines to move data from transactional systems into analytical data warehouses
• Develop real-time and batch data ingestion systems for social media, campaign, and user metrics
• Create multi-tenant data models with strong data isolation and partitioning strategies
• Build and optimize analytical databases for high performance and low-latency queries (sub-second dashboards)
• Implement data quality checks, validation frameworks, monitoring, and alerting
• Design and maintain secure data access using Row-Level Security (RLS) and Role-Based Access Control (RBAC)
• Build authentication and session-based access control for multi-tenant environments
• Develop optimized SQL transformations for BI dashboards and embedded analytics
• Create aggregated tables, materialized views, and denormalized datasets for reporting
• Work with ETL orchestration tools like Apache Airflow, Prefect, or dbt
• Optimize OLAP systems using partitioning, indexing, compression, and query tuning
• Support high-concurrency analytics workloads with large-scale datasets
• Collaborate on BI and analytics integrations for internal teams and enterprise clients
• Ensure compliance with security standards like SOC2 and GDPR