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Technical Expertise Strong hands-on experience with Apache Spark (Spark Core, Spark SQL, Structured Streaming, DataFrames, Dataset APIs). Proven experience building and operating large-scale distributed data processing systems. Strong understanding of Spark performance tuning, partitioning strategies, joins, shuffles, caching, memory management, and resource optimization. Experience with one or more programming languages such as Scala, Java, or Python. Experience with modern data ecosystems including Hadoop, Hive, Iceberg, AWS EMR, AWS Glue, GCP Dataproc, BigQuery, or equivalent technologies. Experience designing and maintaining batch and streaming data pipelines. Strong understanding of distributed systems, cloud-native architectures, and scalability principles. Hands-on experience deploying and managing Spark workloads on AWS and/or GCP. Leadership Experience Proven experience leading engineering teams and delivering complex technical initiatives. Ability to balance hands-on technical leadership with people management responsibilities. Experience driving cross-functional collaboration and influencing technical direction. Demonstrated success managing technical roadmaps, execution planning, and delivery commitments. Scope & Positioning Mid-level management role positioned between Senior Engineer/Technical Lead and Senior Engineering Manager. Responsible for engineering execution excellence and technical leadership of Spark-based data processing platforms. Acts as a key decision-maker for delivery, architecture, operational reliability, and team growth. Preferred Qualifications Experience with AWS services such as EMR, Glue, S3, Lambda, EKS, ECS, Step Functions, and CloudWatch. Experience with GCP services such as Dataproc, BigQuery, Cloud Storage, Dataflow, Pub/Sub, and Composer. Experience managing terabyte-to-petabyte scale data processing environments. Experience with Spark Structured Streaming, real-time analytics, and event-driven data processing. Contributions to Spark optimization, platform engineering, or open-source data ecosystem projects are a plus. .
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.