Cloud-Native Data Architect

2 days ago

Lucknow, Uttar Pradesh, India beBeeDataEngineer Full time ₹ 15,00,000 - ₹ 20,00,000
Job Title: Data Architect for AI Solutions

We are seeking an experienced Data Architect to design, build and manage robust data pipelines that power our AI 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 in a cloud-native environment.

The ideal candidate will have 3-7 years of experience in Data Engineering, building data pipelines and cloud-based data solutions. Strong programming skills in Python, SQL and/or Scala/Java are required, along with expertise in AWS cloud services, including S3, Glue, EMR, Redshift, Athena, Kinesis, Lambda, Step Functions, DynamoDB and RDS.

Additionally, the successful candidate will have experience with data pipeline orchestration tools such as Apache Airflow, AWS Step Functions, Dagster or similar, and proficiency in big data frameworks like Apache Spark, Hadoop or Flink.

The role involves designing, developing and maintaining ETL/ELT pipelines to process structured, unstructured and streaming data in AWS environments. This includes leveraging AWS services to achieve scalable data ingestion, transformation, storage and querying.

The successful candidate will ensure robust data governance, security and quality controls throughout the data lifecycle, supporting regulatory compliance. They will also enable both real-time and batch data processing pipelines to power AI-driven workflows and applications.

Key responsibilities include collaborating closely with AI/ML teams to prepare clean, high-quality datasets optimized for model training, inference and fine-tuning. The role also involves optimizing data infrastructure to achieve high performance, scalability, reliability and cost efficiency.

Other key responsibilities include implementing CI/CD pipelines and data engineering best practices in a cloud-native architecture for automated deployments. Monitoring, debugging and resolving issues in data pipelines is also essential to ensure high availability, reliability and minimal latency.

Maintaining thorough documentation of data pipelines, architecture and processes is crucial to facilitate reproducibility and knowledge transfer.

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