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Data Engineer / Analytics Engineer About blu Cognition: blu Cognition is an AI/ML based start-up specializing in risk analytics, data conversion and data enrichment capabilities. Founded in 2017, by some very senior professionals from the financial services industry, the company is headquartered in the US, with the delivery centre based in Pune. We build all our solutions while leveraging the latest technology stack in AI, ML and NLP combined with decades of experience in risk management at some of the largest financial services firms in the world. Our clients are some of the biggest and the most progressive names in the financial services industry. We are entering a significant growth phase and are looking for motivated and analytical freshers who want to join us in this exciting journey. What will your role involve Work with large structured datasets using SQL and Py Spark. Build, maintain, and optimize ETL/data processing pipelines. Assist in business/entity matching logic and fuzzy matching implementations. Create and validate analytical datasets for model development and reporting. Perform data cleaning, transformation, aggregation, and quality checks. Write efficient SQL queries using joins, CTEs, window functions, and aggregations. Support feature engineering for ML/risk modeling use cases. Work on incremental data processing and monthly/daily refresh strategies. Analyze data discrepancies, debug pipeline failures, and improve reliability. Collaborate with analytics, data science, and engineering teams. Participate in testing, deployment, and code review activities. To help us level up, you will ideally have: A background in Computer Science, Data Science, Statistics, Mathematics, or a related field. Strong SQL knowledge joins, CTEs, aggregations, CASE statements, and window functions. Basic understanding of Python and familiarity with Py Spark or distributed data processing concepts. Understanding of relational databases, data structures, and ETL/data pipeline concepts. Exposure to AWS or cloud platforms such as Redshift, Spark, Hadoop, or Databricks is a plus. Familiarity with Git/version control and basic understanding of APIs. An analytical mindset and strong problem-solving skills, with attention to detail and data accuracy. The ability to work in a fast-paced environment and to deal with ambiguity. Strong communication, documentation, and collaboration skills across multiple teams. .
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.