
Cloud-Native Data Solutions Architect
4 days ago
Job Overview
At our organization, we're seeking a highly skilled Data Engineer to spearhead the design and implementation of robust data pipelines that power our cloud-native solutions. The ideal candidate will have a strong background in data engineering, with 3-7 years of hands-on experience in building data pipelines and cloud-based data solutions.
The role focuses on developing efficient ETL/ELT workflows, ensuring data quality, security, and scalability to support AI/ML model training, inference, and intelligent decision-making processes.
Key Responsibilities:
- Design and develop ETL/ELT pipelines to process structured, unstructured, and streaming data in cloud environments.
- Leverage AWS services such as S3, Glue, EMR, Lambda, Kinesis, Step Functions, Redshift, Athena, DynamoDB, RDS, and Lake Formation for scalable data ingestion, transformation, storage, and querying.
- Implement robust data governance, security, and quality controls throughout the data lifecycle, supporting regulatory compliance.
- Enable real-time and batch data processing pipelines to power AI-driven workflows and applications.
- Collaborate closely with AI/ML teams to prepare clean, high-quality datasets optimized for model training, inference, and fine-tuning.
- Optimize data infrastructure for high performance, scalability, reliability, and cost efficiency.
- Implement CI/CD pipelines and data engineering best practices in a cloud-native architecture for automated deployments.
- Monitor, debug, and resolve issues in data pipelines to ensure high availability, reliability, and minimal latency.
- Maintain thorough documentation of data pipelines, architecture, and processes to facilitate reproducibility and knowledge transfer.
Required Skills & Qualifications:
- 3–7 years of hands-on experience in Data Engineering, building data pipelines, and cloud-based data solutions.
- Strong programming skills in Python, SQL, and/or Scala/Java.
- Expertise in AWS cloud services, including S3, Glue, EMR, Redshift, Athena, Kinesis, Lambda, Step Functions, DynamoDB, and RDS.
- Experience with data pipeline orchestration tools such as Apache Airflow, AWS Step Functions, Dagster, or similar.
- Proficiency in big data frameworks like Apache Spark, Hadoop, or Flink.
- Solid understanding of data modeling, data warehousing concepts, and schema design principles.
- In-depth knowledge of data governance, lineage, security policies (IAM roles, encryption, Lake Formation).
- Hands-on experience in managing real-time streaming data using Kafka, Kinesis, or equivalent tools.
- Familiarity with DevOps practices, including Infrastructure as Code (Terraform, CloudFormation), CI/CD automation, Git, and containerization (Docker).
Preferred Attributes:
- Experience working in regulated industries with a focus on compliance and data privacy.
- Strong problem-solving skills and ability to work in a collaborative team environment.
- Passion for designing scalable and efficient cloud-native data solutions.
- Excellent documentation skills, ensuring clear communication of technical solutions and architecture.
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