About The Opportunity
A fast-scaling enterprise technology firm enabling digital transformation for global clients through cloud-native data architecture and real-time analytics. We specialize in building secure, scalable AWS data pipelines that power AI/ML initiatives, operational BI, and data governance frameworks across finance, healthcare, and logistics verticals. Our engineers work directly with business stakeholders to turn raw data into actionable intelligence—powered by modern AWS services and best-in-class engineering practices.
Role & Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines using AWS Glue, Lambda, Step Functions, and Amazon Redshift/S3.
- Optimize data lake architectures and implement partitioning, compression, and metadata management strategies on S3.
- Automate data ingestion from heterogeneous sources (RDBMS, APIs, Kafka streams) into AWS data warehousing and analytics layers.
- Deploy and monitor data quality checks, lineage tracking, and pipeline observability via CloudWatch, AWS Glue DataBrew, and third-party tools.
- Collaborate with data scientists and analysts to build feature stores, model training datasets, and serve predictive outputs through REST APIs.
- Enforce infrastructure-as-code (IaC) using CloudFormation or Terraform to provision secure, auditable, and reproducible data environments.
Skills & Qualifications
Must-Have
- AWS Glue
- Amazon S3
- Redshift
- SQL
- Python
- CloudFormation
- ETL/ELT
- Spark
Preferred
- AWS Certified Data Analytics – Specialty
- Experience with Apache Airflow or Step Functions
- Familiarity with data mesh or data fabric patterns
Benefits & Culture Highlights
- Work from office with flexible core hours and no commute hybrid policy
- Access to AWS certification reimbursements and hands-on sandbox environments
- Collaborative engineering culture with bi-weekly tech talks and sprint retrospectives
Skills: redshift,etl,pipelines,aws,amazon
