Cloud Data Engineer (Apache Iceberg)

Location: Westlake, TX (Local only) Duration: 12+ Months Contract Rate: $60-70/hr on W2 Job Description Apache Iceberg is the key skill here. Must haves: Strong hands-on experience with Apache Iceberg (table design, evolution, metadata, partitioning). Deep experience with AWS data stack: S3, EMR, Lambda, Glue, IAM, Step Functions, CloudWatch Fluency in Python for data pipelines, automation, and APIs. Experience with distributed engines such as Spark, Flink, or PySpark. Expertise in scalable ETL/ELT pipelines and real-time streaming architectures. Strong SQL and data modeling expertise. Kafka can be a nice to have skill Description: Role Summary We are seeking a highly skilled Data Engineer to design, build, and optimize our modern data platform leveraging Apache Iceberg on AWS, with strong expertise in Spark, Kafka and Python. The ideal candidate has deep experience building scalable, high?quality data pipelines, distributed data processing systems, and table-format–based lakehouse architectures. This role is ideal for engineers who love building robust data foundations, enabling fast and reliable analytics, and working with cutting?edge open data lake technologies. Key Responsibilities 1. Lakehouse Architecture (Apache Iceberg) • Design and build Iceberg-based data lakes with ACID-compliant, versioned datasets. • Implement Iceberg table evolution (schema evolution, partition spec, snapshot management). • Develop best practices for Iceberg governance, metadata compaction, and performance tuning. 1. Data Pipelines & Distributed Processing • Build scalable batch and streaming pipelines using AWS services (S3, EMR, Glue, Lambda, Step Functions). • Develop ingestion and transformation workflows using Python, Spark, or Flink. • Implement CDC pipelines using Kafka Connect or equivalent tooling. • Ensure robust CI/CD integration with GitHub Actions or similar. 1. Streaming Data Engineering (Kafka) • Design and operate Kafka-based streaming pipelines (Kafka/MSK). • Build producers/consumers using Python or JVM languages. • Implement patterns such as topic partitioning, compaction, schema registry, and event versioning. 1. Data Modeling, Quality, and Observability • Design data models for analytical and operational use cases using Iceberg tables. • Implement automated data quality checks, validation rules, and anomaly detection. • Build lineage, monitoring, alerting, and pipeline observability. 1. AWS Architecture & Operations • Apply best practices for AWS security, cost optimization, and data governance. • Manage IAM, KMS, S3 object lifecycle management, networking, and data encryption. • Operationalize EMR/Glue jobs, containerized workloads, or serverless workloads. 1. Cross?Functional Collaboration • Partner with analytics, platform, and product teams to deliver high-quality data products. • Participate in design reviews, architecture discussions, and roadmap planning. • Mentor junior engineers and contribute to engineering standards. Required Qualifications • 4–10+ years of experience in Data Engineering or similar roles. • Strong hands-on experience with Apache Iceberg (table design, evolution, metadata, partitioning). • Deep experience with AWS data stack: • S3, EMR, Lambda, Glue, IAM, Step Functions, CloudWatch • Strong proficiency in Kafka (producers/consumers, schema registry, partitioning strategies). • Fluency in Python for data pipelines, automation, and APIs. • Experience with distributed engines such as Spark, Flink, or PySpark. • Expertise in scalable ETL/ELT pipelines and real-time streaming architectures. • Strong SQL and data modeling expertise.