Senior Data Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 13h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 28d ago
The date the source published, not the day we noticed it (2026-09-03). Last seen at its source 3h ago.
We have tracked this listing since 24 Sep 2026 (7 days). The employer's own board has carried it every time we have read it, most recently 3 hours ago.
Is it remote?
The listing says yes
The location field doesn't say remote, so our assessment is based on the title or the description. Read the listing before applying.
Who may apply?
Hungary
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay PLN 311.6k–399.8k/yr
Middle 50% of 36 listings that do state pay — Engineering · Senior · Eastern Europe · PLN/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
workday employer's own board first seen 7d ago · last seen 3h ago
- location not stated
The listing
The Senior Data Engineer is responsible for designing, building, and maintaining the data pipelines, transformation layers, and data models that power the enterprise lakehouse. This role is a technical anchor on the data engineering team, delivering robust ELT/ETL solutions and serving as a mentor to junior engineers.
KEY RESPONSIBILITIES
▸ Design and implement scalable batch and streaming data pipelines using Apache Spark, Kafka, and Flink
▸ Build and maintain the Bronze/Silver/Gold medallion architecture within the lakehouse (Delta Lake / Iceberg)
▸ Develop and optimize complex SQL and PySpark transformations for large-scale datasets
▸ Integrate structured, semi-structured, and unstructured data sources into the lakehouse
▸ Collaborate with data architects to evolve the physical and logical data models
▸ Implement data quality checks and monitoring using Great Expectations or dbt tests
▸ Write Infrastructure-as-Code for pipeline environments (Terraform, Helm)
▸ Participate in code reviews and enforce engineering standards and best practices
▸ Troubleshoot pipeline failures, performance bottlenecks, and data incidents
▸ Mentor junior and mid-level data engineers and contribute to internal knowledge sharing
REQUIRED QUALIFICATIONS
▸ 6+ years of data engineering experience with a track record of enterprise-scale delivery
▸ Expert proficiency in Python and SQL; PySpark experience required
▸ Hands-on experience with Apache Spark, Delta Lake, or Apache Iceberg
▸ Experience with orchestration tools: Apache Airflow, Prefect, or Dagster
▸ Strong knowledge of cloud data services: AWS Glue, Azure Data Factory, GCP Dataflow
▸ Proficiency with version control (Git), CI/CD pipelines, and containerization (Docker/Kubernetes)
▸ Experience with dbt (data build tool) for transformation layer management
▸ Bachelor's degree in Computer Science, Engineering, or related technical field
PREFERRED QUALIFICATIONS
▸ Experience with Databricks, Snowflake, or Apache Hudi
▸ Knowledge of streaming architectures and Apache Kafka
▸ Certifications: Databricks Certified Data Engineer, AWS Data Analytics Specialty
At Dynata, we are committed to fostering an inclusive, accessible environment, where all employees and customers feel valued, respected and supported. We are dedicated to building a workforce that reflects the diversity of our customers and communities in which we live and serve. Dynata welcomes and encourages applications from people with disabilities. We are committed to an inclusive work culture for all our employees. Accommodations by request can be made for all aspects of the selection process.
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