Big Data Engineer with Scala and Spark

Grid Dynamics Holdings, Inc.; Málaga; On-site; EUR 45,000 - 85,000; Full time

Benefits offered by this job

Opportunity to work on bleeding-edge projects

Work with a highly motivated and dedicated team

Competitive salary

Flexible schedule

Professional development opportunities

Job summary

Grid Dynamics Holdings, Inc. is looking for a motivated individual to develop high-performance data analytics platforms based in Málaga, Andalucía. You will work on big data analytics, developing distributed systems, and automating anti-fraud processes across advertising surfaces.

Ideal candidates should have strong knowledge of Scala, Hadoop, and Spark, as well as apply best practices in data quality. The role offers a competitive salary and flexible schedule, aimed at professionals who thrive in agile environments.

Qualifications

• Strong knowledge of Scala.
• In-depth knowledge of Hadoop and Spark.
• Experience with data mining and stream processing technologies.
• Understanding of best practices in data quality and quality engineering.
• Ability to quickly learn new tools and technologies.
• English language proficiency.

Responsibilities

• Running big data analytics and building large scale data infrastructure.
• Detecting meaningful data patterns.
• Assuring the integrity of our data.
• Measuring fraud activity and its impact on campaign and user performance.
• Analyzing the results of mitigations against fraud.

Skills

Scala

Hadoop

Spark

Kafka

Spark Streaming

Akka Streams

Tools

Unix-based operating systems

JVM build systems

Job description

Our customer is one of the world’s largest technology companies based in Silicon Valley with operations all over the world. In this project, we are working on the bleeding edge of Big Data technology to develop a high-performance data analytics platform, which handles petabytes datasets.

Project description

Advertising Platforms group makes it possible for people around the world to easily access informative and imaginative content on their devices while helping publishers and developers promote and monetize their work. Today, our technology and services power advertising in Search Ads in of the biggest search and news providers. Our platforms are highly-performant, deployed at scale, and setting new standards for enabling effective advertising while protecting user privacy. We are looking for an ambitious, self-starter individual who can thrive in an agile environment. You will develop distributed systems to establish, refine and automate our anti-fraud processes across our advertising surfaces.

Responsibilities

• Running big data analytics, and building large scale data infrastructure
• Detecting meaningful data patterns
• Assuring the integrity of our data
• Measuring fraud activity and its impact on campaign and user performance
• Analyzing the results of mitigations against fraud

Requirements

• Strong knowledge of Scala
• In-depth knowledge of Hadoop and Spark, experience with data mining and stream processing technologies (Kafka, Spark Streaming, Akka Streams)
• Understanding of the best practices in data quality and quality engineering
• Ability to quickly learn new tools and technologies
• English languages are required

Will be a plus

• Knowledge of Unix-based operating systems (bash/ssh/ps/grep etc.)
• Experience with JVM build systems (SBT, Maven, Gradle)

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Professional development opportunities

About us

Grid Dynamics is the engineering services company known for transformative, mission-critical cloud solutions for retail, finance and technology sectors. We architected some of the busiest e-commerce services on the Internet and have never had an outage during the peak season. Founded in 2006 and headquartered in San Ramon, California with offices throughout the US and Eastern Europe, we focus on big data analytics, scalable omnichannel services, DevOps, and cloud enablement.