Senior Data Engineer

Dynata; United States; On-site; USD 130,000 - 150,000; Full time

Benefits offered by this job

Medical benefits

Discretionary incentive program

Job summary

Dynata is seeking a Senior Data Engineer to design, build, and maintain data pipelines, data models, and the enterprise lakehouse. You will deliver robust ELT/ETL solutions and mentor junior engineers, shaping scalable batch and streaming workflows.

Equipped with Python/SQL expertise and PySpark, you will work with Spark, Delta Lake, and Iceberg, integrating diverse data sources, enforcing quality checks, and collaborating with data architects to evolve data models.

Qualifications

• 6+ years of data engineering experience with enterprise-scale delivery.
• Expert proficiency in Python and SQL; PySpark experience required.
• Hands-on with Spark, Delta Lake, or Iceberg.
• Experience with Airflow, Prefect, or Dagster.
• Strong knowledge of cloud data services (AWS, Azure, GCP).
• Git, CI/CD, and containerization (Docker/Kubernetes).
• dbt for transformation layer management.
• Bachelor's degree in Computer Science, Engineering, or related field.

Responsibilities

• Design and build scalable batch and streaming data pipelines using Spark, Kafka, and Flink.
• Develop lakehouse architecture and ETL/ELT solutions with Delta Lake/Iceberg.
• Create and optimize SQL and PySpark transformations for large datasets.
• Integrate diverse data sources into the lakehouse and evolve data models.
• Implement data quality checks and monitoring.
• Write infrastructure as code (Terraform, Helm).
• Participate in code reviews and enforce standards.
• Troubleshoot pipelines and mentor junior engineers.

Skills

Python

SQL

PySpark

Apache Spark

Kafka

Flink

Delta Lake

Iceberg

Airflow

Prefect

Dagster

AWS Glue

Azure Data Factory

GCP Dataflow

Git

CI/CD

Docker

Kubernetes

dbt

Databricks

Snowflake

Apache Hudi

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

Apache Spark

Delta Lake

Apache Iceberg

Airflow

Prefect

Dagster

Git

CI/CD pipelines

Docker

Kubernetes

dbt

Databricks

Snowflake

Apache Kafka

AWS Glue

Azure Data Factory

GCP Dataflow

Job description

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 deliver the highest quality first-party data to help businesses around the world gain precise insights, activate the right audiences, and confidently measure impact. With industry-leading respondent accuracy, reliability, and a commitment to continuous improvement, Dynata is the trusted foundation for smarter decision-making.

At Dynata, we are committed to creating an inclusive and accessible environment where every employee and customer feels valued, respected, and supported. We strive to build a workforce that reflects the diversity of the communities we serve. Dynata welcomes and encourages applications from individuals with disabilities and is dedicated to fostering a work culture that supports everyone. Accommodations are available upon request for all aspects of the selection process.

Dynata is an Equal Opportunity Employer. We consider all qualified applicants and employees without regard to race, color, religion, sex (including pregnancy, sexual orientation, and gender identity), national origin, marital status, age, disability, genetic information, veteran status, or any other legally protected status under applicable laws.

The base salary range for this position in is $130K-$150K/yr; however, base pay offered may vary depending on location, job-related knowledge, skills, and experience. A discretionary incentive program may be provided as part of the compensation package, in addition to a full range of medical and other benefits, dependent on full-time employment status.