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Responsibilities Engage with clients to understand business goals, data challenges, and requirements, and translate them into scalable data and analytics solutions. Design and implement data architecture, ETL/ELT pipelines, data integration, and data platforms to support analytics, AI, and machine learning use cases. Perform data analysis, exploratory data analysis, feature engineering, statistical analysis, and predictive modeling to generate actionable business insights. Develop, evaluate, and support machine learning models, from data preparation and experimentation through deployment and monitoring. Ensure data quality, governance, security, and reliability across data and analytics solutions. Collaborate with AI Engineer, Analysts, and Software Engineer to deliver end-to-end solutions. Lead project teams, manage timelines and deliverables, and communicate technical and analytical concepts to clients and stakeholders. Mentor junior team members and stay up to date with emerging technologies in Data Engineering, Data Science, AI, and Cloud. Other tasks as assigned. Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Engineering, or related fields. Proven experience in Data Engineering, Data Science, Analytics, or Data/AI Consulting. Strong Python or Scala skills with experience in data processing, analysis, and automation. Strong knowledge of SQL, data modeling, ETL/ELT, databases, and scalable data pipelines. Experience with Apache Spark, Kafka, Airflow, or similar technologies. Experience in machine learning, statistical analysis, predictive modeling, and data visualization. Familiarity with ML frameworks such as Scikit-learn, XGBoost, TensorFlow, or PyTorch is a plus. Experience with AWS, Azure, or GCP is a plus. Strong analytical, problem-solving, communication, and client-facing skills. Experience leading projects or multidisciplinary teams is a plus.
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.