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Our client is seeking a Data Engineer with 5+ years of hands-on experience to build an AI-enablement layer on top of its enterprise Finance data platform. The role focuses on designing, building, and operating trusted datasets, semantic models, governed data products, and embedded natural-language analytics using Snowflake and/or Databricks . This is a data platform engineering role , not a data science or model-building role. The successful candidate will build the governed data foundation that makes enterprise AI reliable, secure, and accessible to Finance business users. Key Responsibilities Build AI-ready, governed data products on Snowflake and/or Databricks with clearly defined KPIs, hierarchies, and business semantics. Develop semantic layers and trusted datasets for BI reporting and natural-language querying. Configure and optimize Snowflake Cortex capabilities and/or Databricks Genie spaces for accuracy and business relevance. Build RAG pipelines and conversational analytics applications using Streamlit or Databricks Apps. Develop and maintain ETL/ELT pipelines using dbt, Airflow, Snowpark, PySpark, SQL, and Python. Implement data governance controls, including RBAC, row- and column-level security, masking, lineage, metadata management, and auditability. Collaborate with Finance stakeholders while optimizing platform performance, compute costs, and LLM usage. Required Skills 5+ years of hands-on data engineering experience on cloud data platforms. Demonstrated real-world project delivery experience with Snowflake and/or Databricks . Direct hands-on experience building, configuring, and tuning Snowflake Cortex capabilities or Databricks Genie spaces with semantic models in production or advanced pilots. Proven experience delivering semantic layers and trusted, governed data products. Strong understanding of KPI definitions, business hierarchies, data semantics, and business glossary alignment. Strong hands-on expertise in dbt, PySpark, Snowpark, SQL , and Python . Experience with Airflow, Databricks Workflows , or an equivalent orchestration tool. Experience implementing data governance controls such as RBAC, row-level security, data masking, lineage, and auditability. Data governance experience within regulated enterprise environments. Experience integrating structured and unstructured data, including PDFs, SharePoint or Microsoft Teams content, and enterprise knowledge sources, into AI-enablement workflows. Nice-to-Have Skills Experience in pharma, life sciences, regulated financial services, or another highly regulated domain. Experience integrating Veeva CRM, IQVIA, SAP, clinical data, or similar enterprise data sources. Experience building business-facing analytics applications using Streamlit or Databricks Apps. SnowPro Advanced or Databricks Data Engineer Professional certification. Experience with LangChain, LlamaIndex, or an equivalent RAG framework. Experience optimizing data-platform compute, including warehouse sizing, cluster tuning, and query performance. Knowledge of LLM cost optimization through token management, caching, and model routing. Familiarity with Finance data models, KPIs, reporting processes, and governance requirements. About YMinds.AI YMinds.AI is a technology-driven talent and consulting organization focused on connecting skilled professionals with opportunities in Artificial Intelligence, data engineering, cloud technologies, and digital transformation. We work with clients to build high-performing teams capable of delivering scalable, secure, and future-ready technology solutions. Keywords Data Engineer, Snowflake, Databricks, Snowflake Cortex, Databricks Genie, PySpark, SQL, Python, dbt, Airflow, ETL/ELT, RAG, Data Governance, Semantic Models Hashtags #DataEngineer #Snowflake #Databricks #DataEngineering #SnowflakeCortex #DatabricksGenie #HyderabadJobs #Hiring #YMindsAI
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.