Data Architect

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.