Senior Data Infrastructure / Data Platform Engineer

Job Title : Data Infrastructure / Data Platform Engineer Location : Irving, TX Duration : 18+ months contract Job Description Senior Data Infrastructure Engineer to lead the design and development of highly scalable, cloud-enabled data platforms.

This is a senior individual contributor role, ideal for a hands-on engineer with deep technical expertise in big data, cloud infrastructure, containerized workloads, and real-time processing frameworks.

Must-Have Qualifications: Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred). 7+ years of hands-on experience in data engineering with a focus on data platform architecture, ETL/ELT, and data modeling.

Proficiency in programming languages such as Python, Java, Scala, and Shell scripting.

Expertise in big data tools: Kafka, Spark, HDFS, HBase, Flink.

Experience with data orchestration tools like Apache Airflow, Kubeflow, dbt, or SQLMesh.

Hands-on experience with containerization and orchestration using Docker and Kubernetes.

Deep understanding of hybrid/multi-cloud infrastructure with practical experience deploying pipelines on AWS, Azure, or GCP.

Strong command of SQL and experience with both relational (e.g., PostgreSQL, MySQL) and NoSQL (e.g., HBase, DynamoDB) databases.

Proven track record in implementing data governance, monitoring, and security best practices across distributed systems.

Preferred Qualifications: Experience with data visualization tools such as Tableau, Power BI, or Looker.

Background in mentoring or technically leading other data engineers on project delivery or code quality.

Familiarity with infrastructure as code practices (e.g., using Terraform or CloudFormation). Senior Data Infrastructure Engineer to lead the design and development of highly scalable, cloud-enabled data platforms.

This is a senior individual contributor role, ideal for a hands-on engineer with deep technical expertise in big data, cloud infrastructure, containerized workloads, and real-time processing frameworks.

Key Responsibilities: Design, build, and maintain high-performance, scalable data infrastructure supporting real-time and batch data workflows.

Develop robust ETL/ELT pipelines using tools like Apache Airflow, dbt, and SQLMesh.

Implement and manage distributed data processing solutions using Apache Spark, Apache Flink, Kafka, and HDFS/HBase.

Optimize data storage formats and systems, including Parquet, Avro, NoSQL (e.g., HBase, Cassandra), and relational databases, for analytics and operational workloads.

Build and maintain data lakes and data warehouses across hybrid environments, including on-prem, AWS, Azure, and Google Cloud Platform.

Utilize Kubernetes and Docker to orchestrate scalable, containerized data services.

Monitor and enforce data quality, lineage, and governance through automated pipelines and logging frameworks.

Collaborate with data scientists, analysts, and business stakeholders to define and deliver data solutions aligned with business goals.

Mentor junior engineers through code reviews, architectural guidance, and hands-on support.

Mandatory Skills Data Platform Architecture, ETL/ELT, Data Modeling, Python/Java/Scala or Shell Scripting, Kafka/Spark/HDFS/HBase or Flink experience, Data Orchestration tool experience Apache Airflow/Kubeflow/DBT or SQLMesh, Hands-on containerization and orchestration using Docker and Kubernetes, Deep hybrid/multi-cloud infrastructure understanding, Experience deploying pipelines on AWS/Azure or GCP, SQL, Relational PostgreSQL and MySQL, NoSQL HBase or DynamoDB Desirable Skills Data Visualization, Experience with Tableau/Power BI or Looker, Experience mentoring or technically leading others, Familiarity with infrastructure as code practices (e.g., using Terraform or CloudFormation).