Data Engineer
2 weeks ago
Key Responsibilities
- Design, build, and maintain ETL/ELT pipelines for structured and unstructured data in AWS.
- Work with AWS services (e.g., S3, Glue, EMR, Lambda, Kinesis, Step Functions, Redshift, Athena, DynamoDB, RDS) to manage data ingestion, transformation, and storage.
- Ensure data quality, governance, and security across the full lifecycle.
- Enable real-time and batch data processing to support AI-driven workflows.
- Collaborate with AI/ML teams to prepare datasets for model training, inference, and fine-tuning.
- Optimize data infrastructure for scalability, cost-efficiency, and performance.
- Implement CI/CD pipelines and best practices for data engineering in a cloud-native environment.
- Monitor and troubleshoot data pipelines to ensure high availability and reliability.
Required Skills & Qualifications
- 3–7 years of experience in data engineering (adjust based on seniority).
- Strong programming skills in Python, SQL, and/or Scala/Java.
- Expertise in AWS cloud ecosystem (S3, Glue, EMR, Redshift, Athena, Kinesis, Lambda, etc.).
- Experience with data pipeline orchestration tools (Airflow, Step Functions, Dagster, or similar).
- Proficiency with big data frameworks (Spark, Hadoop, or Flink).
- Familiarity with data modeling, warehousing, and schema design.
- Solid understanding of data governance, lineage, and security (IAM, Lake Formation, encryption).
- Experience with real-time streaming data (Kafka, Kinesis, or equivalent).
- Knowledge of DevOps practices (Terraform/CloudFormation, CI/CD, Git, Docker).
Job Type: Full-time
Pay: ₹1,500, ₹3,041,108.58 per year
Work Location: Remote
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