Master's degree in Computer Science, Management Information Systems, or related field of study, or equivalent experience
Designing and building scalable ETL/ELT pipelines for structures, semi-structures, and unstructured data.
Working with cloud data platforms such as Snowflake Databricks, or AWS Redshift
Using data integration tools like Informatica or AWS Glue
Developing in distributed data processing frameworks such as Apache Spark
Proficiency in SQL and at least one programming language such as: Python, Scala, or Java
Strong understanding of software engineering best practices, including:
Unit testing and test-driven development (TDD)
Continuous integration/Continuous Deployment (CI/CD)
Source control using tools like Git and platforms like GitHub
Experience with cloud services (preferably AWS), including:
S3, Lambda, Step Functions, Fargate, DynamoDB, SNS/SQS, and CloudWatch.
Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).
Experience deploying and operationalizing machine learning models in production environments]
Familiarity with business intelligence tools such as PowerBI, Tableau, or Foundry for data visualization and reporting.
Experience working with geospatial data and tools such as ArcGIS
Experience with data cataloging, metadata management, and data lineage tools such as informatica and Collibra.