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Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, were helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Data Engineering Overview Ethoca, a Mastercard company, is seeking a Senior Data Engineer to join the First Party Trust (FPT) team. First Party Trust serves Merchants, Financial Institutions (Issuers and Acquirers), and Digital Partners by enabling the identification and mitigation of First Party Fraud across the payments ecosystem. The platform leverages modern cloud-based data technologies to ingest, process, secure, enrich, and analyze large-scale transactional and behavioral data. The team develops scalable data pipelines, data products, analytics capabilities, and AI-ready data platforms while ensuring compliance with Mastercard security, privacy, and data governance standards. Our technology stack includes Snowflake, Databricks, Apache Airflow, Apache NiFi, Azure Data Factory (ADF), UDAP, SQL, Python, Spark, Azure Cloud Services, File Transfer Technologies, Splunk, Dynatrace, Terraform, and Data Security Platforms. Are you motivated by solving complex data challenges at enterprise scale Do you enjoy building highly scalable and resilient data platforms Do you want to leverage cloud-native data technologies to enable analytics, AI, and machine learning solutions Do you want to be part of a team transforming how global merchants and financial institutions combat fraud through data-driven insights We are looking for innovative problem-solvers who thrive in a collaborative environment and are passionate about building secure, scalable, and reliable data solutions. Role Design, develop, implement, and maintain enterprise-scale data pipelines, data platforms, and analytical data products that support business intelligence, fraud analytics, machine learning initiatives, and operational reporting. Partner with business, product, architecture, and engineering teams to ensure secure, scalable, and compliant data solutions across the organization. Major Accountabilities Design and develop scalable batch, streaming, and near-real-time data pipelines using modern cloud data engineering technologies. Build, maintain, and optimize enterprise data platforms leveraging Snowflake, Databricks, Spark, Airflow, NiFi, ADF, and related technologies. Design and implement robust ETL/ELT frameworks for ingesting, transforming, validating, and delivering data across multiple business domains. Collaborate with business stakeholders, data consumers, product owners, and architects to understand data requirements and deliver reliable data solutions. Develop and maintain data models, data marts, and curated datasets that support analytics, reporting, AI, and machine learning use cases. Integrate and manage data from multiple internal and external sources while ensuring data quality, consistency, lineage, and governance. Implement data orchestration and workflow automation using Apache Airflow, Azure Data Factory, NiFi, and related technologies. Develop and optimize Spark and Databricks workloads to process large-scale datasets efficiently. Monitor and tune data pipelines, database workloads, and platform performance to maximize scalability, reliability, and cost efficiency. Implement enterprise data governance standards, including data quality controls, metadata management, and lineage tracking. Ensure compliance with PCI-DSS, privacy regulations, Mastercard security standards, and enterprise data classification requirements. Implement and maintain encryption, decryption, tokenization, masking, and secure data handling processes for sensitive and regulated data. Support secure file transfer mechanisms and integrations using SFTP, managed file transfer solutions, APIs, and cloud-native services. Develop resilient data recovery, disaster recovery, and operational support processes across critical data platforms. Support production environments through incident management, root-cause analysis, troubleshooting, and continuous service improvement activities. Analyze platform operational metrics using Splunk, Dynatrace, and monitoring tools to identify reliability and performance improvement opportunities. Participate in architecture reviews, code reviews, design reviews, and technology evaluations. Create and maintain technical documentation, data flow diagrams, architecture artifacts, design specifications, and operational runbooks. Support AI and machine learning initiatives by building trusted, high-quality, feature-rich datasets and data .
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.