AWS Data Specialist
New
J
JobgetherIT Security
BrazilFull-Time
Salary not disclosed
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Job Details
- Required Skills
- AWSSQLApache AirflowDynamoDBSparkPySpark
Requirements
- Proven professional experience in data engineering, with strong hands-on expertise designing and implementing solutions within the AWS data ecosystem.
- Strong knowledge of AWS data services, including AWS Glue, EMR, Amazon Athena, DynamoDB, Lake Formation, Amazon S3, and EventBridge.
- Solid experience with Apache Spark and PySpark for large-scale data processing.
- Experience with data warehouse and modern data architecture concepts, including Amazon Redshift, Lakehouse architectures, Open Table Formats such as Iceberg or Delta, data contracts, and schema evolution.
- Practical experience with Apache Airflow and strong knowledge of relational databases and SQL, as well as NoSQL technologies.
- Understanding of data governance, data lineage, and observability practices, including OpenLineage, CloudWatch, or Datadog.
- Strong DevOps and automation mindset, with the ability to troubleshoot incidents, improve processes, and balance cost against performance and scalability.
- Ability to collaborate effectively with multidisciplinary teams and communicate technical concepts clearly.
- Strong planning, analytical, and problem-solving skills, with a proactive approach to continuous improvement.
Responsibilities
- Plan and execute the migration of systems and large volumes of data to modern AWS cloud infrastructure.
- Design, develop, optimize, and orchestrate robust ETL/ELT pipelines while ensuring data integrity, quality, reliability, and performance.
- Build and maintain scalable data architectures using AWS services and modern processing and storage technologies.
- Work closely with development, product, operations, and other multidisciplinary teams to deliver efficient and sustainable data solutions.
- Monitor data environments, pipelines, and databases, investigate complex incidents, and implement solutions that improve reliability and operational performance.
- Create and maintain technical documentation covering architecture, data lineage, processes, and operational procedures.
- Apply data governance and observability practices to improve transparency, quality, and maintainability across data environments.
- Contribute to capacity planning and technical decisions by evaluating cost, scalability, and performance trade-offs.
- Promote DevOps principles, automation, and continuous improvement throughout data engineering workflows.
- Explore and support the adoption of emerging technologies in Generative AI and advanced data architectures.
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