Senior Data Engineer
Dynata Open At a glance
Senior Data Engineering role at Dynata. Remote, Hungary · full-time.
Pay not stated Data Engineer salary
dataroles summary, based on the employer's posting.
What you'll do
- Build batch and streaming pipelines, then maintain the lakehouse layers and data models they feed
- Transform large datasets with SQL and PySpark, bringing structured, semi-structured and unstructured sources together
- Work with data architects to shape data models and add checks that catch quality issues
- Write infrastructure code, review engineers’ changes and investigate pipeline failures or slowdowns
- Guide junior and mid-level engineers and share technical knowledge with the team
What you bring
- At least six years delivering data engineering work at enterprise scale
- Strong Python and SQL skills, including practical PySpark experience
- Hands-on work with Spark and either Delta Lake or Iceberg
- Experience with an orchestration tool, cloud data services, Git, CI/CD, containers and dbt
- A bachelor's degree in computer science, engineering or another related technical field
Who this fits
This senior role suits an experienced engineer who can deliver data pipelines and models while mentoring less experienced teammates. The role is remote in Hungary.
From the employer
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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