7 days ago
Bengaluru, IndiaStaff+
Responsibilities
- Design, develop, and operate scalable, modular data and analytics platforms.
- Lead end-to-end cloud-native data solutions on AWS across ingestion, transformation, orchestration, and consumption layers.
- Build and maintain high-performance ETL/ELT pipelines using AWS Glue, Lambda, S3, Step Functions, EventBridge, and related technologies.
- Design and optimize Amazon Redshift data warehouses, including schemas, performance, workload management, partitioning, and query optimization.
- Define technical architectures, data integration patterns, data models, governance practices, data quality standards, and engineering best practices.
- Support analytics, reporting, AI/ML, and strategic decision-making through data analysis, profiling, and modeling.
- Advise, mentor, and coach data engineering and analytics teams.
- Participate in backlog grooming, sprint planning, technical design reviews, and effort estimation.
- Evaluate and deploy emerging data engineering, cloud, and analytics technologies.
- Promote innovation, collaboration, automation, engineering excellence, and continuous improvement.
Requirements
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical discipline.
- At least 7 years of hands-on data engineering experience with enterprise-scale ETL/ELT pipelines, data platforms, data lakes, and data warehouses.
- At least 5 years of hands-on experience developing cloud-native data solutions on AWS, including S3, Redshift, Glue, Lambda, IAM, CloudWatch, and EventBridge.
- At least 4 years of programming experience in Python, Scala, or Java with strong software engineering fundamentals.
- At least 3 years of experience designing, optimizing, and supporting Amazon Redshift data warehouse solutions.
- At least 3 years of experience with dimensional modeling, star and snowflake schemas, data vault methodologies, and analytical data structures.
- At least 2 years of experience designing batch and streaming architectures for real-time or near-real-time processing.
- Experience with AWS Glue, Apache Airflow, Kafka, Spark, or equivalent cloud-native services.
- Experience with at least two database technologies and cloud data warehouse platforms.
- Experience with source control, CI/CD, infrastructure-as-code, and automated deployment practices.
- Proficiency with Tableau and Amazon QuickSight.
- Understanding of data quality, governance, metadata, lineage, and monitoring.
- Preferred experience includes healthcare or regulated environments, privacy and security regulations, healthcare interoperability, EMR/EHR and PACS integrations, MLOps, AI/ML platforms, and modern data platforms.
- AWS certifications such as AWS Certified Data Engineer, AWS Solutions Architect, or AWS Analytics Specialty are highly desirable.
Benefits
- Work hours are 1 PM to 10 PM IST.
- Relocation assistance is provided.
Tech Stack
Amazon RedshiftApache AirflowApache KafkaApache SparkAWSGoogle BigQueryJavaMongoDBMySQLPostgreSQLPythonScalaSnowflake
Categories
Data Engineering