Senior Data Platform Engineer for Scalable Analytics

iTradeNetwork, Inc.; Charlotte (NC); Hybrid; USD 166,000 - 172,000; Full time

Benefits offered by this job

Health insurance

401(k) matching

Paid parental leave

Flexible PTO

Job summary

iTradeNetwork, Inc. is hiring a Staff Data Engineer to lead the design, build, and optimization of enterprise-scale data infrastructure.

You will architect secure, scalable data systems powering analytics, reporting, ML, and data products, while mentoring engineers and shaping long-term data strategy. You will collaborate with engineering leadership, data scientists, analysts, and product managers to translate business needs into robust data solutions, driving performance, cost efficiency, and

Qualifications

• 10+ years of professional experience in data/engineering roles.
• Experience architecting large-scale data platforms for analytics, reporting, and ML.
• Deep expertise in ETL/ELT pipelines, data warehouses, and data lakes.
• Strong background in distributed processing (Spark/Flink/Hadoop) and real-time systems (Kafka/Kinesis).
• Ownership of end-to-end engineering workstreams from design to deployment.

Responsibilities

• Architect and build scalable data platforms (data lakes, warehouses, streaming).
• Define end-to-end data pipelines for real-time and batch processing.
• Set standards for data governance, quality, and metadata.
• Collaborate with product, data science, and analytics teams to productionize models.

Skills

10+ years experience

SQL

Python/Java/Scala

AWS data stack

Data modeling

Governance & quality

Airflow/dbt

Education

Master's degree or higher in CS/Engineering

Tools

Airflow

dbt

Spark

Kinesis

Redshift

S3

Job description

iTradeNetwork, Inc. is hiring a Staff Data Engineer to lead the design, build, and optimization of enterprise-scale data infrastructure.

You will architect secure, scalable data systems powering analytics, reporting, ML, and data products, while mentoring engineers and shaping long-term data strategy. You will collaborate with engineering leadership, data scientists, analysts, and product managers to translate business needs into robust data solutions, driving performance, cost efficiency, and