Jobs → Data & Analytics → Data Architect

About the Role We are seeking a hands-on Data Architect to design and lead AWS-based data platforms. This role requires strong expertise in Python, PySpark, Spark optimization, and AWS, along with financial domain experience. You will be responsible for architecture, development, performance, governance, and mentoring data engineers. Key Responsibilities • Design end-to-end AWS data architecture covering ingestion, transformation, storage, and consumption. • Define data modeling, lakehouse, partitioning, and file format standards. • Develop production-grade Python and PySpark pipelines. • Optimize Spark jobs, AWS Glue, Lambda, Airflow DAGs, and S3 performance. • Manage scalability, security, reliability, and AWS costs. • Build reusable frameworks for logging, error handling, reconciliation, and data quality. • Review code, mentor engineers, and establish CI/CD and testing standards. • Handle financial data pipelines involving transactions, ledgers, payments, and regulatory reporting. • Ensure data governance, lineage, auditability, encryption, and access controls. Requirements • 9+ years in Data Engineering/Architecture, including 3–4 years in Lead/Architect roles. • Strong Python, PySpark, SQL, and Apache Spark performance tuning. • Experience with AWS Glue, S3, Lambda, IAM, CloudWatch, and AWS Data Catalog. • Proficiency in Apache Airflow and workflow orchestration. • Knowledge of Data Lake, Data Warehouse, Lakehouse, and Dimensional Modeling. • Familiarity with Git, CI/CD, testing, Terraform or CloudFormation. • Financial domain knowledge: Banking, Capital Markets, Insurance, Payments, or Lending. • Understanding of reconciliation, regulatory reporting, audit trails, and data privacy. • Strong communication, leadership, and mentoring skills. Good to Have • Experience with Redshift, Athena, EMR, Lake Formation, Step Functions, Kafka/Kinesis, Iceberg, Hudi, Delta Lake, and AWS certifications.