aws data engineer - leading consultancy firm.

I am currently partnering with an established organization leveraging advanced technical systems to drive data-driven decision-making and cross-functional analytics solutions.

• Design & Optimize Architecture: Build, scale, and continuously improve robust data frameworks, pipelines, and analytical data warehouses to support enterprise-wide analytical needs.
• Data Processing & Pipeline Engineering: Create and deploy end-to-end ETL/ELT workflows, batch scripts, and data ingestion processes for both structured and unstructured data assets.
• Performance Tuning: Conduct database optimization, schema design, and PL/SQL/SQL tuning to ensure low-latency application performance and reliable data flows.
• Support Modeling Teams: Clean, massage, and curate raw data into highly available formats, directly enabling data scientists to perform descriptive and predictive modeling.
• Asset Governance: Maintain and organize comprehensive data catalogs to ensure data assets are highly accessible, secure, and easy to retrieve for all business stakeholders.

• Core Technical Experience: Minimum of 3 years of hands-on experience in data engineering, warehousing, database development (Oracle SQL/PLSQL), and cloud infrastructure—specifically within AWS ecosystems (EC2, S3, Redshift, Athena, EMR, Kinesis).
• DevOps & Infrastructure: At least 3 years of experience in Kubernetes-based DevOps environments, container orchestration, microservices deployment, and CI/CD engineering.
• Programming & Tooling: Proficient in SQL, Python, or R for data aggregation, alongside familiarity with enterprise data frameworks (e.g., Spark, Hadoop, HDFS, Airflow, Hive, Scala, or Java).
• Data Management: Proven capability in web crawling, building data lakes, managing real-time streaming solutions, and handling diverse datasets (structured/unstructured).
• Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical discipline.

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