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About RedotPay RedotPay is a global crypto payment fintech integrating blockchain solutions into traditional banking and finance infrastructure. Our user-friendly crypto platform empowers millions globally to spend and send crypto assets, ensuring faster, more accessible, and inclusive financial services. RedotPay advances financial inclusion for the unbanked and supports crypto enthusiasts, driving the global adoption of secure and flexible crypto-powered financial solutions. Join us in shaping the future of finance and making a meaningful impact on a global scale. Job Description We are looking for an experienced, self-driven Data Warehouse Engineer. You will be responsible for the architectural design of the company's core data warehouse, data model development, and optimization of ETL data pipelines. Responsibilities Data Warehouse Modeling & Development: Design and develop the company-level data warehouse models, including building the ODS, DWD, DWS, and ADS layers, ensuring the data models are scientific, stable, and scalable. ETL Pipeline Development: Build efficient and stable ETL/ELT data processing workflows, write high-quality data processing scripts, and ensure timely data delivery (SLA compliance). Business Data Support: Deeply understand the business, collaborate closely with Product, Operations, BI, and Data Analytics teams, accurately capture data requirements, and provide agile data mart support and metric system development. Performance Tuning & Maintenance: Perform SQL optimization and Hive/Spark job performance tuning in large-scale data environments, resolving issues such as data skew, scheduling delays, and resource waste. Data Quality & Governance: Participate in the construction of data quality monitoring systems (DQC), manage metadata, map data lineage, ensure consistency of data definitions, and maintain the accuracy of data assets. Requirements Bachelor’s degree or above in Computer Science, Mathematics, Statistics, or related fields. 3+years of experience in data warehousing or big data development. Solid theoretical foundation in data warehousing, with a deep understanding of dimensional modeling Proficient in designing fact tables, dimension tables, Slowly Changing Dimensions (SCD), and subject domains. Excellent SQL writing and extreme performance tuning skills. Proficient in Hive, Spark, and other big data computing frameworks. Familiar with the working principles of HDFS and YARN. Good to have at least one mainstream big data scheduling system such as Dolphin Scheduler, Airflow, or Azkaban.
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.