Data Architect

Info Dinamica Inc

Hartford, CT, United States

1 day ago

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Role details

Contract type

Temporary contract

Employment type

Full-time (> 32 hours)

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Agile Methodology Amazon Web Services Amazon Elastic Compute Cloud Amazon S3 Batch Processing Big Data Continuous Integration Data Architecture Data Integration Extract Transform Load (ETL) Data Systems Data Warehousing

+22 more

Database Queries Distributed Systems Identity and Access Management Industry Standard Architecture Python (Programming Language) NoSQL Cloud Services Software Engineering Data Streaming Data Processing Data Ingestion Apache Spark Git Data Lakes Kubernetes Apache Flink Apache Kafka Functional Programming Cloudwatch Stream Processing Data Pipelines Docker

Requirements

Strong hands-on experience in Python for data engineering and application development. Extensive experience with AWS cloud services, including S3, EMR, Glue, Lambda, IAM, EC2, ECS/EKS, CloudWatch, and Redshift. Strong expertise in Apache Spark for large-scale batch data processing. Hands-on experience with Apache Flink for real-time stream processing and event-driven data pipelines. Experience designing and implementing batch and streaming data architectures. Strong knowledge of data modeling, data warehousing, and data lake/lakehouse concepts. Experience with ETL/ELT frameworks and data integration. Strong SQL skills and experience with relational and NoSQL databases. Experience with Apache Kafka or similar messaging/event streaming platforms. Strong understanding of distributed computing and big data technologies. Experience with Docker, Kubernetes, and CI/CD pipelines. Hands-on experience with Git and Agile development methodologies. Design and implement scalable, secure, and high-performance data architecture solutions on AWS. Build and optimize batch processing pipelines using Apache Spark. Develop real-time streaming data solutions using Apache Flink. Design end-to-end data ingestion, transformation, and processing pipelines. Define data models, governance standards, and architectural best practices. Python, Apache Spark, AWS, Apache Flink, batch and streaming data architectures, ETL/ELT, Apache Kafka, Docker, Kubernetes, CI/CD pipelines.