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Job Summary We are looking for an Analytics Engineer / Business Analyst to bridge business requirements and data-platform implementation. This role will work closely with business users, Data Engineers, Data Architects, Data Quality teams, and Analytics teams to translate business needs into clear data requirements, data mappings, transformation logic, and analytics-ready datasets. The successful candidate should have a strong understanding of data, business processes, and SQL, and be able to analyze source data, define business rules, validate outputs, and ensure that datasets delivered by the Data Platform are suitable for business, reporting, analytics, and downstream use cases. The role is expected to take ownership of data requirements from initial discussion through implementation and validation, including requirement clarification, source-to-target mapping, business-rule definition, data reconciliation, UAT support, and documentation. Your work will directly empower reporting, advanced analytics, and data-driven decision-making across the organization. Key Responsibilities · Requirement Gathering & Translation - Gather, analyze, and translate business requirements into clear data requirements, source-to-target mappings, transformation rules, and acceptance criteria. · Source Data Analysis & Profiling - Analyze source data and business processes to identify required fields, relationships, data gaps, data-quality considerations, and downstream impacts. · Data Transformation & Modeling - Perform SQL-based data analysis, reconciliation, and validation to confirm that delivered data meets business requirement · Testing & Release Support - Support System Integration Testing (SIT), User Acceptance Testing (UAT), production validation, data migrations, and historical data backfill activities to ensure accurate and smooth data rollouts. · Business Logic & Glossary Management - Document business rules, logic, and metric definitions clearly. Collaborate with the Data Governance team to ensure the Business Glossary is accurate and reflects actual data implementations. · Cross-Functional Collaboration - Work closely with Data Engineers to ensure the right raw data is ingested, and with Data QA / Data Architects to define business-driven Data Quality rules and validation criteria. Qualifications · At least 3 years of experience in Business Analysis, Data Analysis, Analytics Engineering, BI, Data Warehousing, Data Integration, or related roles. · Strong ability to understand complex business processes and translate them into technical data requirements. Excellent stakeholder management and communication skills. · Solid understanding of data modeling concepts and strong SQL skills for data analysis, joins, aggregations, validation, reconciliation, and troubleshooting. · Experience gathering and documenting data-related business requirements. Source-to-target mappings, data definitions, transformation rules, or functional specifications. · Strong analytical, communication, documentation, and stakeholder-management skills. · Ability to work effectively with business users, Data Engineers, Data Architects, Data Quality teams, and Analytics teams. Nice-to-Have · Deep understanding of the Banking, Fintech, or Financial Services sectors. · Familiarity with modern data warehouse or lakehouse concepts · Experience with Apache Iceberg, or similar data platforms. · Basic Python or PySpark knowledge for data analysis or validation. · Familiarity with data quality, data governance, metadata, lineage, business glossary, or data classification. Experience with Jira, Confluence, GitLab, or similar collaboration and documentation tools.
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.