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Job Title: Senior Data Engineer - Spark / Kafka / NiFi Overview: Job Posting Title: Senior Data Engineer - Spark / Kafka / NiFi Who is Mastercard Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our culture is guided by the Mastercard Wayown it, simplify it, sense of urgency, thoughtful risk taking, unlock potential, and be inclusive. ________________________________________ Job Summary We are seeking a highly skilled Senior Data Engineer with deep expertise in Testing, validating, Developing and supporting large-scale batch and real-time data platforms built on Apache Spark, Apache Kafka, and Apache NiFi. The ideal candidate will have a strong background in distributed systems, streaming architectures, event-driven platforms, and cloud-native data processing. The successful candidate will collaborate with cross-functional teams to design innovative solutions, enhance platform capabilities, and ensure operational excellence in production environments. Key Responsibilities Develop, test, validate and maintain, scalable batch and real-time data processing applications using Apache Spark, Kafka, and NiFi. Build validation suite for high-performance, fault-tolerant, and resilient distributed systems capable of handling large data volumes. Develop reusable frameworks, libraries, and shared platform components to accelerate engineering productivity. Translate business and technical requirements into scalable software solutions. Contribute to architecture discussions, technical designs, and engineering standards. Streaming & Data Platform Engineering Create and implement validation suite for Spark batch and streaming applications to process high-volume datasets efficiently. Validate and support Apache NiFi data ingestion, transformation, and routing workflows. Build reliable data pipelines supporting real-time and near-real-time processing requirements. Implement solutions for data replay, recovery, checkpoint management, and failure handling. Performance & Scalability Analyze system bottlenecks and optimize application performance, throughput, and resource utilization. Improve scalability, reliability, and availability of distributed applications. Develop and test solutions leveraging modern storage technologies including Apache Ozone, Ceph, and cloud-native storage platforms. Build deployment automation and operational tooling to improve platform reliability. Monitor production environments and proactively address operational concerns. Participate in troubleshooting, root-cause analysis, and incident resolution activities. Engineering Excellence Participate in code reviews and promote engineering best practices. Maintain high-quality documentation for systems, APIs, and platform components. Collaborate closely with Product, Architecture, Platform, and DevOps teams. Contribute to CI/CD processes and continuous improvement initiatives. Mentor junior engineers and foster a culture of technical excellence and innovation. Required Qualifications Bachelor's degree in Computer Science, Engineering, or related technical field. 6+ years of hands-on software deployment, validation, testing, development experience on large-scale data-intensive applications. Strong expertise in Apache Spark (Batch and Structured Streaming). Strong experience with Apache Kafka and event-driven architectures. Experience developing, testing and supporting Apache NiFi data flows. Proficiency in Scala, pyspark or Python. Strong SQL and data modeling skills. Experience working with distributed storage systems such as Apache Ozone, Ceph, HDFS, or cloud-based object stores. Hands-on experience with Linux, Git, shell scripting, and CI/CD pipelines. Strong analytical, problem-solving, and communication skills. Experience operating production-grade distributed systems in cloud or hybrid-cloud environments. Preferred Qualifications Experience building observability solutions using monitoring and logging platforms. Knowledge of data governance, metadata management, and data platform best practices. Experience with containerization and orchestration technologies (Docker, Kubernetes). Knowledge of performance engineering, resilience testing, and production readiness assessments. Experience building engineering automation frameworks and reusable validation platforms. Familiarity with observability tools, monitoring systems, and operational analytics. Experience in highly regulated, transaction-processing, or large-scale enterprise environments. To find salary ranges and other disclosures for US and Europe countries where applicable, visit .
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.