Data Architect
Summary
Designs and implements scalable enterprise data architectures using AWS, Databricks, and Python, building data lakes, pipelines, and APIs for analytics and AI initiatives.
Key Responsibilities
- Design and implement scalable enterprise data architectures across cloud and on-premises environments.
- Develop modern data lake, lakehouse, and data warehouse solutions using AWS and Databricks.
- Build and maintain batch and real-time data pipelines using Python, PySpark, AWS Glue, Lambda, Step Functions, and Kafka.
- Design REST API and event-driven integration solutions for enterprise applications.
- Lead cloud migration and modernization initiatives, including Oracle to Redshift and Databricks migrations.
- Implement data governance, security, metadata management, and data quality frameworks.
- Collaborate with business stakeholders, solution architects, and development teams to deliver scalable data solutions.
- Support analytics, reporting, AI/ML, and business intelligence initiatives.
- Mentor technical teams and provide architecture guidance throughout project delivery.
Required Skills
- 10+ years of experience in Data Architecture, Data Engineering, or Integration Architecture.
- Strong experience with AWS services such as Glue, Lambda, Redshift, S3, EMR, DMS, Athena, and Step Functions.
- Hands-on experience with Databricks, Delta Lake, Unity Catalog, and Delta Live Tables.
- Strong knowledge of Python, PySpark, Spark, SQL, and ETL tools.
- Experience with Kafka, Talend, Informatica, Airflow, and Terraform.
- Expertise in REST APIs, Microservices, Event-Driven Architecture, and CDC.
- Experience with data modelling, data warehousing, and lakehouse architectures.
- Good understanding of CI/CD, DataOps, DevOps, and cloud security best practices.
- Excellent stakeholder management and communication skills.