Lead Data Engineer
2 months ago
Qualifications :
- 5+ years experience delivering data platforms at scale
- 5+ years experience architecting, building and releasing production-grade data solutions within a SaaS environment
- Demonstrated, hands-on mastery of the AWS suite of data engineering tools including Athena, Glue, Kinesis, RedShift, S3 etc.
- Extensive experience with DBT for the development of data processing pipelines
- Experience with Infrastructure as Code (IaC) using Terraform to define and deploy infrastructure related to data solutions on AWS
- Experience with Apache Iceberg, Apache Hudi or Delta Lake
- Deep experience building batch and data streaming architectures
- Ability to assert and defend opinions on the merits of differing solutions
- Ability to clearly explain technical concepts to non-technical audiences
- Familiarity with a variety of third-party data engineering tools and common architectures
- Excellent communication skills necessary to manage business requests and work with different teams across different geographies and time-zones; experience working with remote and distributed teams will be an added advantage
- Hands-on working knowledge and experience is required in:
- ELT Processes (DBT, Spark, etc.)
- Relational Databases (PostgreSQL, MySQL, SQL, PLSQL, etc.)
- NoSQL Databases (Redis, Elastic Search, Mongo, etc.)
- Architecture patterns such as Lambda/Kappa/Delta
- Programming Languages (Python, Scala, Spark, etc.)
- Streaming Technologies (Kafka, Kinesis, etc.)
- Agile Methodologies (Scrum, TDD, BDD, etc.)
- Cloud Platforms (AWS, Azure, GCP), preferably AWS
- Development of Complex Big Data Solutions like Spark, RedShift, EMR, etc. and Open-Source Platforms like Hudi, etc.
- Experience with several of the following tools/technologies is desirable:
a. Amazon QuickSight
b. Git, Jira, GitHub, Jupyter Notebooks
b. Data Structures and Machine Learning Models
- Knowledge of the following technologies is a plus:
a. Unix/Linux environments
b. Continuous Integration and Continuous Delivery Tools like GitHub Actions, etc.
c. Containerization Technologies like ECS, Docker and Kubernetes
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