Senior Data Engineer

Stack

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

Requirements

• ▹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

Nice to have

• ▹Experience with Databricks, Snowflake, or Apache Hudi
• ▹Knowledge of streaming architectures and Apache Kafka
• ▹Certifications: Databricks Certified Data Engineer, AWS Data Analytics Specialty

Soft skills

About the company

Dynata is committed to fostering an inclusive, accessible environment where all employees and customers feel valued, respected, and supported.