Senior Data Engineer
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Before you apply
LocationRemote - Hungary
Work moderemote
Typefull-time
SenioritySenior
Experience6+ yrs
Company size1,001–5,000 people
PostedSep 3, 2026 · 2mo ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
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
Experience with Databricks, Snowflake, or Apache Hudi
Knowledge of streaming architectures and Apache Kafka
Certifications: Databricks Certified Data Engineer, AWS Data Analytics Specialty
Skills
Apache Spark
Kafka
Flink
Delta Lake
Iceberg
PySpark
SQL
Great Expectations
dbt
Terraform
Helm
Git
Docker
Kubernetes
AWS Glue
Azure Data Factory
GCP Dataflow
Databricks
Snowflake
Apache Hudi
Apache Airflow
Prefect
Dagster
Python
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
Legal
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.