At a Glance
- Tasks: Build scalable data pipelines and cloud-native platforms using AWS services.
- Company: Join a forward-thinking tech company focused on data innovation.
- Benefits: Competitive salary, flexible hours, remote work options, and growth opportunities.
- Other info: Collaborative environment with a focus on professional development.
- Why this job: Make an impact in data engineering while working with cutting-edge technologies.
- Qualifications: Proficiency in Python, SQL, and AWS services required.
The predicted salary is between 121500 - 148500 $ per year.
We are seeking a skilled AWS Data Engineer with expertise in building scalable data pipelines and cloud-native platforms.
The ideal candidate will have hands-on experience with AWS services such as Redshift, Glue, RDS, Lambda, and Airflow, and will be responsible for designing, implementing, and maintaining robust data engineering workflows that support analytics, reporting, and machine learning initiatives.
Key Responsibilities
- Design and implement scalable ETL/ELT pipelines using AWS Glue, Lambda, Step Functions, and related services.
- Integrate structured and unstructured data from diverse sources into data lakes and warehouses (e. g., S3, Redshift, RDS, Athena).
- Build and maintain cloud infrastructure for data analytics platforms using Terraform, Cloud Formation, or similar Ia C tools.
- Develop reusable frameworks and automation scripts to streamline data operations.
- Data Analytics Enablement
- Collaborate with data engineers, data scientists, and analysts to deliver high-quality platforms for data loading, reporting, and machine learning.
- Optimize data models and queries for performance and scalability.
- Monitoring and Maintenance
- Monitor data pipelines and troubleshoot issues to ensure reliability and data integrity.
- Implement CI/CD pipelines for data engineering workflows using Git Lab, Bitbucket, Jenkins, or Git Hub Actions.
- Security and Governance
- Ensure compliance with data governance and security best practices.
- Implement access controls and encryption for sensitive data.
- Required Qualifications
- Proficiency in Python, SQL, and Py Spark.
- Hands-on experience with AWS services: S3, Glue, Lambda, Redshift, RDS, Athena, Step Functions.
- Experience with orchestration tools like Apache Airflow or MWAA.
- Familiarity with Dev Ops tools and practices (CDK, CI/CD, Git, Terraform).
- Strong understanding of data modeling, warehousing, and ETL processes.
- Preferred Qualifications
- Experience with big data tools (Spark, Hive, Kafka, Python, Type Script).
- Knowledge of containerization (Docker, Kubernetes).
- Familiarity with data visualization tools (e. g., Power BI).
- AWS certifications (e. g., AWS Certified Data Analytics – Specialty).
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