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Coforge

Data Architect

Pay: Not specified

Posted: 3 weeks ago

Location: Fort Mill, SC (Onsite)

Last Updated: 4 days ago

Hours: Full-Time

Expires: 11/25/2026

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Job Description

Data Architect at Coforge Data Architect at Coforge in Fort Mill, South Carolina Posted in 15 days ago.

Type:

full-time Data Architect with AWS Data platform experience

Experience:

+12

Years Skills:

:

Data Architect, AWS, Kafka, Python, Apache Spark Location:

Fort Mill SC We at Coforge are hiring Data Architect with AWS Data platform experience with the following skill sets. Job Description Design, develop, and support scalable enterprise data platforms using AWS cloud-native services and modern data engineering technologies. Architect Data Lake and Lakehouse solutions using Amazon S3, AWS Glue, Lake Formation, Athena, Redshift, Apache Iceberg, Delta Lake, and Parquet. Develop scalable batch, incremental, and Change Data Capture pipelines for ingesting and processing data from databases, APIs, files, enterprise applications, and streaming platforms. Build and optimize ETL and ELT workflows using AWS Glue, Apache Spark, PySpark, Python, SQL, Lambda, and distributed data-processing frameworks. Design reusable data ingestion, transformation, validation, reconciliation, exception-handling, and error-recovery frameworks. Implement real-time and near-real-time data ingestion and processing solutions using Kafka, Amazon MSK, Amazon Kinesis, Lambda, SNS, SQS, and EventBridge. Develop event-driven solutions supporting integration between business applications, operational platforms, analytical systems, and downstream data consumers. Implement workflow orchestration using Apache Airflow, Amazon

MWAA, AWS

Step Functions, and event-based scheduling mechanisms. Design and maintain metadata management, schema management, data cataloging, data lineage, and data discovery capabilities using AWS Glue Data Catalog and Lake Formation. Implement data partitioning, compaction, retention, lifecycle management, and storage optimization strategies to improve performance and cost efficiency. Establish data quality controls for completeness, accuracy, consistency, integrity, uniqueness, and source-to-target reconciliation. Implement secure and governed data-access models using

IAM, KMS, S3

policies, Lake Formation permissions, encryption, and fine-grained access controls. Collaborate with Business, Data Governance, Security, Analytics, Infrastructure, and Architecture teams to deliver trusted and reusable enterprise data products. Tune data pipelines, Spark workloads, data-storage layouts, and analytical queries for scalability, reliability, and performance. Lead architecture reviews, technical design discussions, coding standards, platform modernization, and engineering best-practice initiatives. Provide technical leadership and mentoring to data engineers while ensuring alignment with enterprise architecture, security, governance, and delivery standards.