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Key Responsibilities Own the end-to-end technical design and delivery of AI-powered business analytics applications, enabling a complete analytical loop from conversational data querying to anomaly attribution, insight generation and decision recommendations. Lead the design and implementation of the intelligent attribution engine, covering dimensional attribution (multi-dimensional drill-down with contribution calculation), factor attribution (metric calculation-tree decomposition) and correlation-based attribution, to automatically identify root causes of key metric fluctuations. Design automated metric monitoring and anomaly detection mechanisms, enabling AI to proactively surface issues and push attribution findings to stakeholders. Lead the build-out of the metrics semantic layer, defining machine-readable business definitions, calculation logic, analysable dimensions and synonyms, so that natural-language intent is accurately translated into structured queries. Package data and analytical capabilities into standardised tool interfaces (Function Calling / MCP protocol) for consumption by AI applications. Build and maintain business analytics data models, taking ownership of dimensional modelling, metric processing and data quality assurance. Establish evaluation and observability frameworks to quantify query accuracy, metric-definition correctness and reliability of attribution conclusions, driving a closed-loop bad-case optimisation process. Implement controls over AI data access, including permission boundaries, data masking, data lineage, result explainability and full audit trails, in line with Hong Kong licensed corporation regulatory requirements. Define data and AI technical standards, mentor junior team members, and drive cross-departmental alignment on metric definitions. Requirements Bachelor’s degree or above in Computer Science, Data, Statistics or a related discipline; minimum 8 years of experience in data engineering or data intelligence, including at least 2 years leading projects independently. Expert-level SQL (complex queries, window functions, performance tuning) and strong proficiency in Python. Deep expertise in dimensional modelling and data warehouse layering (ODS/DWD/DWS/ADS), with proven experience independently leading a complete data domain or data platform build. Solid LLM application engineering capability, with in-depth understanding of RAG, Function Calling / Tool Calling and Text-to-SQL — including their implementation approaches, capability boundaries and failure modes — and the ability to constrain probabilistic generation with deterministic rules. Hands-on experience with at least one data warehouse or compute engine (Hive, MaxCompute, Greenplum, ClickHouse, Databricks, etc.) and orchestration.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.