Every year, we help hundreds of thousands of people find rewarding jobs in the ever-changing world of work.
We understand the importance of a job in peoples lifes and we want to help them find work that feels good. And we’ll help them continue to grow as their needs and ambitions change.
At Randstad, our value comes from our people and that is why we put them first. We are proud of our learning culture and career architecture framework that encourages ours team to develop both personally and professionally.
We believe that talent grows when presented with opportunity and this is why we encourage our people to think beyond their role. We have created a culture that enables talent to flourish, encouraging entrepreneurship, fostering team spirit, and continually building mutual trust.
designing and building data pipelines and data storage solutions, using multiple database technologies across our GCP and AWS platforms (AWS/OpenSearch, BigQuery, Datastore, GCS)
collaborating with our data scientists to design and build ML pipelines, that bring our models alive
ensuring the full compliance of solutions being implemented with corporate data management principles, particularly pertaining to security and privacy
defining and appropriating work to the product roadmap and backlogs
training and coaching junior data engineers
collaborating within the data engineering community within the Randstad ecosystem, on sharing best practices, setting and deploying standards, and helping grow our collective data engineering skills
who will you work with
You will be a part of a highly engaged, international and agile team of highly skilled specialists, including data scientists, data engineers, machine learning engineers, and data analysts.
You will work closely with other data engineers within the Randstad ecosystem, both within projects as in the data engineering community.
what you will bring
Masters degree in Computer science or related technical studies (M.Sc. or equivalent experience)
min. 5 years work experience as a data engineer.
algorithmic thinking, analytical capabilities.
cloud platforms: comprehensive understanding of cloud computing (min. 3 years experience with GCP, AWS as a plus), solid experience in distributed computing.
coding: high proficiency in Python, SQL and Java; Scala is a plus
data processing: good knowledge on Apache Beam; Apache Spark as a plus.
data management: good grasp of data governance strategies, ETL tooling like dbt, orchestration (Airflow).
source control: experience working with CI/CD pipelines, gitlab, agile methodology.
ML engineering: experience with implementing MLOps practices and ML models deployments into production.
strong knowledge of data structures, data platforms and deployment at scale.
hands on experience on designing and building data pipelines, including transformation, processing and quality control.
broad knowledge on applying the latest DataOps concepts, implementation toolings and data management frameworks.
language skills: English (verbal and written)
Is this the job for you? We would love to hear from you! Please apply directly to the role and we will get in touch with you.
...
