Senior Data Engineer

Big Data Engineer Experience: 5–8 Years Location: Texas Contract : W2 We’re hiring a Big Data Engineer to design, build, and optimize large-scale distributed data systems that support advanced analytics, AI/ML workloads, and real-time business applications. The ideal candidate brings hands-on expertise with Spark, cloud big data stacks, streaming frameworks, data lakehouse architectures, and enterprise-grade data engineering practices. Responsibilities: • Build scalable ETL/ELT pipelines, data ingestion frameworks, and orchestration workflows. • Develop batch & streaming data pipelines using Kafka, Spark Streaming, Flink, or Kinesis. • Build and optimize data lakes & lakehouses using Delta Lake, Iceberg, or Hudi. • Deploy and manage distributed data systems on AWS/GCP/Azure. • Optimize big data compute environments (Spark, EMR, Databricks, Snowflake, Redshift, BigQuery). • Implement data quality, metadata management, lineage tracking, and governance controls. • Support ML engineers & data scientists with feature pipelines and model-ready datasets. • Enable analytics workloads across BI, dashboards, real-time insights, and predictive models. Requirements: • 5–8 years in Big Data or Data Platform Engineering. • Strong in Spark (PySpark/Scala), SQL, and distributed computing. • Hands-on experience with AWS/GCP/Azure big data services. • Experience with Snowflake, Redshift, BigQuery, and data lakehouse architectures. • Proficient with Kafka, Flink, Kinesis, Airflow, dbt, or similar tools. • Strong understanding of ETL/ELT pipelines, data modeling, storage formats (Parquet/Delta), and performance optimization. • Experience with CI/CD, Docker, and infrastructure automation.