Data Engineer (Up to SGD$800)

They are a global online forex and CFD brokerage company that provides traders with access to a wide range of financial markets, including foreign exchange, cryptocurrencies, commodities, indices, and stock CFDs. With a focus on delivering frictionless trading experiences, Monaxa offers advanced trading platforms such as MT4, MT5, and cTrader, along with services like copy trading, PAMM accounts, and partnership programs. Role Overview We are seeking an experienced Data Engineer to take technical ownership of our reporting database environment and partner closely with our BI Analyst. Our CRM provider manages our core CRM database, and a third-party database vendor manages an operational, real-time replica dedicated to reporting and Power BI analytics. We frequently encounter database stability and performance bottlenecks, and the vendor often attributes them to internal reporting queries without sufficient technical proof. In this role, you will act as the technical subject matter expert between our internal BI team and the database vendor. You will independently verify query performance, optimise reporting schemas and indexes on the reporting replica, design robust data pipelines, and ensure Power BI reports perform reliably without disrupting database synchronisation.

Database Performance & Replication Reliability - Act as the primary technical owner of the reporting database environment (the real-time replica). - Deep-dive into database performance issues, slow-running queries, locks, blocks, and deadlocks between analytical queries and the real-time replication process. - Analyse execution plans, indexing strategies, statistics, memory grants, and I/O pressure on the reporting database. - Triage replication lag and assess whether slowdowns stem from replication pipeline overhead, database sizing, or concurrent analytical workloads. BI Enablement & Data Modelling - Partner directly with the BI Analyst to optimise data retrieval patterns for Power BI reports and dashboards. - Review and refine complex SQL views, stored procedures, and Power Query queries used by the BI team. - Design and implement indexing strategies tailored specifically for analytical query patterns (e.g., covering indexes, filtered indexes, partitioned tables). - Guide architecture decisions regarding Power BI connectivity (Import mode, DirectQuery, incremental refreshes, aggregations). - Design intermediate data marts, aggregate tables, or analytical data layers if necessary to decouple raw replicated tables from heavy dashboard queries. Vendor Management & Incident Accountability - Serve as the technical point of contact for the third-party database vendor managing the replica. - Independently review and validate vendor claims regarding "scripting issues" using query plan hashes, DMV data, wait statistics, and session metrics. - Enforce standard Root Cause Analysis (RCA) procedures with the vendor, holding them accountable for verifiable evidence rather than conjecture. - Collaborate with the vendor to ensure server configurations, tempdb allocation, and maintenance plans (reindexing, statistics updates) support analytical query loads. Data Integrity & Governance - Verify data consistency and latency between the live CRM source and the real-time reporting replica. - Establish automated monitoring and alerting for replication lag, failed refreshes, and query regressions. - Document data schemas, report dependencies, data dictionary definitions, and operational runbooks.

- Experience: 4+ years of hands-on experience in Data Engineering, Database Performance Tuning, or BI Engineering. - Relational Databases & SQL Mastery: Deep expertise in SQL (SQL Server / PostgreSQL /MySQL / Oracle) with a proven track record of tuning complex queries and reading execution plans. - Concurrency & Locking Mechanics: Strong understanding of isolation levels (e.g., Read Committed Snapshot Isolation / Snapshot Isolation), locking, blocking, and how OLAP/reporting queries interact with transactional replication streams. - Power BI & Data Modelling: Solid understanding of Star Schema design (Kimball), Power BI query folding, DirectQuery vs. Import mode, and incremental refresh mechanics. - Workflow Orchestration & Apache Airflow: Hands-on experience designing, developing, scheduling, monitoring, and troubleshooting production data pipelines using Apache Airflow, including DAG development, dependency management, retries, alerting, logging, and failure recovery. Experience administering or supporting an internally managed Airflow environment is highly desirable. - Python for data engineering: Strong Python skills for ETL/ELT development, automation, data validation, API integrations, and operational scripting. - Git and deployment practices: Experience with Git-based version control, code review, environment management, and preferably CI/CD for SQL, Python, and data pipeline deployments. - Pipeline observability and ownership: Experience implementing logging, monitoring, alerting, retry mechanisms, SLA tracking, and failure notifications for production data pipelines. - Vendor & Stakeholder Engagement: Experience acting as a technical liaison between external managed service providers and internal business/analytics teams.