Sr. Data Engineer

Description

We are looking for an accomplished Sr. Data Engineer to join a team building resilient, scalable data platforms in Dallas, Texas. This role focuses on creating efficient batch and streaming solutions across modern cloud and distributed data environments while partnering closely with analytics, data science, and infrastructure teams. This is a Long-term Contract position offering the opportunity to shape reliable data foundations that support critical business insights and operational decisions.

Responsibilities:
Build and enhance robust data pipelines that support ingestion, transformation, and delivery using platforms such as Airflow, NiFi, Databricks, and Spark.
Create streaming and event-driven data solutions with Kafka and Flink to enable timely processing of high-volume data flows.
Architect and refine storage patterns across Hadoop and Amazon S3 with attention to scalability, performance, and cost control.
Establish monitoring, validation, and governance practices that strengthen data quality, security, and operational reliability.
Coordinate complex workflow orchestration across hybrid and multi-cloud environments, including enterprise data processing operations where needed.
Work with structured and semi-structured file types such as Parquet and Avro to improve usability and interoperability across systems.
Diagnose bottlenecks in distributed processing environments and implement improvements that increase efficiency and stability.
Provide technical guidance to less experienced engineers and contribute to a strong engineering culture centered on quality and continuous improvement.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related technical discipline.
At least 7 years of experience in data engineering with a strong record of designing and supporting production data pipelines.
Advanced hands-on knowledge of Kafka, Airflow, NiFi, Databricks, Spark, Hadoop, Flink, and Amazon S3.
Proficiency in Python plus working ability in Scala or Java for automation, transformation, and data processing tasks.
Solid SQL skills and experience working with both relational and NoSQL database technologies.
Background supporting data engineering solutions in on-premises and Kubernetes-based environments.
Understanding of data modeling, governance practices, and data quality controls.
Strong analytical, troubleshooting, and communication skills; experience in hybrid or multi-cloud settings is preferred.

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