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Key Responsibilities End-to-End Data Analysis & Modeling: Design, build, and optimize end-to-end data pipelines and data marts for business intelligence and reporting needs. Data Environment & Processing: Work hands-on within Databricks environments to process, transform, and analyze large-scale datasets using Python (PySpark/Pandas) and SQL. Front-End Application Development: Develop responsive, performant, and interactive front-end interfaces/dashboards to display key data metrics and analytical workflows to stakeholders. Cross-Functional Collaboration: Partner with global cross-functional teams (Product, Data Science, Design) in an Agile environment across different time zones. Ownership & Initiative: Drive feature delivery independently, taking full ownership of code quality, architecture decisions, and project timelines. Qualifications & Skills Bachelor’s or Master’s degree in Computer Science, Data Science, IT, or related fields Minimum 5 years of experience in Data Engineering, Data Analytics, or related roles Intermediate in English communication. Hands-on Databricks Experience: Deep familiarity with Databricks platform capabilities, PySpark , workspace notebooks, and job scheduling. SQL Expertise: Advanced proficiency in writing complex, highly optimized SQL queries, window functions, CTEs, and data modeling. Python Mastery: Strong experience writing clean, modular Python scripts for data manipulation, ETL processes, and analysis (using libraries like Pandas, NumPy, or PySpark). End-to-End Analytics: Proven track record of taking raw, unstructured data and delivering clear, actionable business insights or user-facing data products. Front-End Tech Stack (Preferred) Core Front-End: Proficiency in HTML5, CSS3, and JavaScript (ES6+) / TypeScript. Modern Frameworks: Hands-on experience with React.js (Preferred) or Vue.js for building dynamic user interfaces. Data Visualization Frameworks: Experience integrating charting libraries like Recharts, D3.js, Chart.js, or Plotly. Preferred / Good-to-Have Experience with cloud platforms (AWS / GCP / Azure). Familiarity with version control workflows (Git, GitHub/GitLab) and CI/CD pipelines. Understanding of Data Governance, Data Quality frameworks, and API integration (REST APIs/GraphQL) between front-end and back-end data services. Onsite Mon-Thu (WFH Fri) 09.00-18.00 (Flexible Times)
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.