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Open nowPosted 6 hours ago

Data engineer

randstadhu278 open roles

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The clock on this job

Early applications get read.

13.5% of postings close within 7 days. Measured by our own scanner across the market. randstadhu postings stay open a median of 14 days.

Share of postings closed within
  1. 2.3%1 day
  2. 4.6%3 days
  3. 13.5%7 days
  4. 21.8%14 days
  5. 36.6%30 days
  6. 58.9%60 days
This job: posted 6 hours ago

randstadhu median: 14 days open

The posting

What We Offer

  • Base salary supplemented with a Cafeteria package (SZÉP kártya).
  • Company-provided private health insurance.
  • Modern office environment in Budapest, with 1 day of Home Office per week available after successfully passing your probation period.
  • Home Office Kit provided for your remote work (monitor, docking station).
  • Continuous access to various corporate discounts and special offers (e.g., AYCM employee sports pass and much more).
  • Core Engineering Background: Minimum 5 years of in-depth experience in Java or Python and PySpark.
  • Data Pipelines & Warehousing: Minimum 5 years of experience in data engineering pipelines, building data warehouse systems, understanding core ETL principles, and writing complex SQL queries.
  • GCP Big Data Ecosystem: Minimum 5 years of hands-on experience in GCP-based Big Data deployments (batch and real-time) utilizing BigQuery, Bigtable, Google Cloud Storage, Pub/Sub, Data Fusion, Dataflow, Dataproc, and Dataform.
  • Data Architecture: Minimum 5 years of development experience utilizing Data Warehousing and Big Data ecosystems for GCP.
  • Infrastructure as Code (IaC): At least 1 year of experience deploying Google Cloud services using Terraform.
  • Workflow Orchestration: Minimum 2 years of experience with Apache Airflow and/or Astronomer.
  • Relational Databases & Schemas: Proven experience building high-performance data pipelines, optimizing relational schemas, and managing data warehouses using PostgreSQL.
  • Mentorship & Leadership: Demonstrated track record of mentoring fellow data/software engineers and driving systemic technical improvements across teams.

The Data Engineer role is focused on delivering end-to-end data solutions—designing, implementing, and optimizing data storage solutions, landing and manipulating data, creating data products, and providing product support. You will support a business-critical data analytics product used across Europe to model vehicle build plans to maximize profitability while strictly complying with EU and local CO2 emissions regulations.

Working within small, cross-functional teams, you will collaborate closely with Product Owners, business customers, IT, and data architects to translate business goals into scalable data solutions using agile, lean, and modern software engineering practices.

Key Responsibilities:

  • Design and build production data engineering solutions and pipeline patterns leveraging Google Cloud Platform (GCP) services (including BigQuery, Dataflow / Apache Beam, Pub/Sub, Bigtable, Data Fusion, Dataproc, Dataform, Cloud Composer / Apache Airflow, Cloud SQL, Compute Engine, Cloud Functions, and App Engine).
  • Migrate existing Big Data pipelines into the Google Cloud Platform environment.
  • Act as an integral part of the implementation team from concept to operations, delivering deep technical subject matter expertise for data platform deployments.
  • Implement automation across all pipeline stages to minimize development and production turnaround times.
  • Identify, develop, evaluate, and summarize Proof of Concepts (PoCs) to validate proposed architectural solutions.
  • Test, benchmark, and compare competing solutions, articulating clear points of view on optimal technical directions.
  • Guide and enhance incident management, deployment processes, and root cause analysis.
  • Collaborate directly with Product Owners, business stakeholders, and architects to design, develop, test, and deploy changes while fostering a highly collaborative team culture.
  • Identify and adopt modern tools and engineering practices that increase agility and delivery speed.
  • Lead the large-scale solutioning and operationalization of data warehouses, data lakes, and analytics platforms on GCP.
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