Design and architect data storage solutions, including databases, data lakes, and warehouses, using AWS services such as Amazon S3, Amazon RDS, Amazon Redshift, and Amazon DynamoDB, along with Databricks Delta Lake. Integrate Informatica IDMC for metadata management and data cataloging.
Create, manage, and optimize data pipelines for ingesting, processing, and transforming data using AWS services like AWS Glue, AWS Data Pipeline, and AWS Lambda, Databricks for advanced data processing, and Informatica IDMC for data integration and quality.
Integrate data from various sources, both internal and external, into AWS and Databricks environments, ensuring data consistency and quality, while leveraging Informatica IDMC for data integration, transformation, and governance.
Develop ETL (Extract, Transform, Load) processes to cleanse, transform, and enrich data, making it suitable for analytical purposes using Databricks Spark capabilities and Informatica IDMC for data transformation and quality.
Monitor and optimize data processing and query performance in both AWS and Databricks environments, making necessary adjustments to meet performance and scalability requirements. Utilize Informatica IDMC for optimizing data workflows.
Requirements
Good experience in data engineering, with expertise in AWS services, Databricks, and/or Informatica IDMC.
Proficiency in programming languages such as Python, Java, or Scala for building data pipelines.
Evaluate potential technical solutions and make recommendations to resolve data issues especially on performance assessment for complex data transformations and long running data processes.
Strong knowledge of SQL and NoSQL databases.
Familiarity with data modeling and schema design.
AWS certifications (e.g., AWS Certified Data Analytics - Specialty, AWS Certified Data Analytics - Specialty), Databricks certifications, and Informatica certifications are a plus.
