Data Engineer

Intellisoft Inc

Malvern, PA, United States

about 1 month ago

Role details

Contract type

Temporary contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Working hours

Regular working hours

Job source

dice.com

Tech stack

Airflow Amazon Web Services Amazon S3 Business Analytics Applications Apache HTTP Server Big Data Continuous Integration Directed Acyclic Graph (Directed Graphs) Information Engineering Data Governance Extract Transform Load (ETL) Data Systems

+28 more

Data Warehousing Dimensional Modeling Github Apache Hive Identity and Access Management Python (Programming Language) Performance Tuning Query Optimization Workflow Management Systems Cloud Platform System Delivery Pipeline Snowflake Electronic Medical Records Cloudformation Data Lakes Pyspark Semi-structured Data Information Technology Deployment Automation AWS Glue Star Schema Functional Programming Cloudwatch Terraform Data Pipelines Jenkins Amazon Redshift Databricks

Requirements

MUST HAVE 12 YEARS OF IT EXPERIENCE AND QUICK SIGHT EXPERIENCE

Senior Data Engineer with 12+ years of experience designing and delivering enterprise-scale data warehousing, ETL/ELT, and cloud-native analytics solutions using AWS Redshift, S3, Glue, Lambda, Airflow, PySpark, and Python.
Strong hands-on expertise with Amazon Redshift, including schema design, distribution keys, sort keys, query optimization, workload management, Redshift Spectrum integration, and performance tuning for large-scale analytical workloads.
Designed and implemented scalable ETL/ELT pipelines using AWS Glue, PySpark, Python, Airflow, Lambda, and S3, enabling efficient ingestion, transformation, and processing of high-volume structured and semi-structured data.
Extensive experience in advanced SQL development, data modeling, dimensional modeling (Star/Snowflake Schemas), data warehousing, and optimization of complex analytical queries supporting enterprise reporting and business intelligence.
Strong AWS experience with S3, Glue, Lambda, IAM, CloudWatch, EMR, Athena, EventBridge, and Redshift, building secure, scalable, and highly available cloud-based data platforms.
Developed and optimized large-scale data processing solutions using PySpark, Spark SQL, Databricks, and EMR, improving processing performance, scalability, and cost efficiency across enterprise data ecosystems.
Built and maintained Apache Airflow DAGs for workflow orchestration, scheduling, dependency management, monitoring, and automated recovery of complex data pipelines.
Experienced in modern Data Lake and Lakehouse architectures leveraging S3, Delta Lake, Apache Iceberg, Snowflake, and Databricks, supporting scalable analytics and data governance initiatives.
Implemented CI/CD and Infrastructure as Code solutions using Terraform, GitHub Actions, Jenkins, AWS CodePipeline, and CloudFormation, enabling automated deployment and management of data engineering workloads.
Proven ability to collaborate with cross-functional teams to deliver robust, high-performance data solutions that support analytics, reporting, operational intelligence, and business-critical decision-making.