Design, build, and maintain data pipelines (batch & streaming) in a cloud-native environment for ingestion, transformation, and processing of large-scale datasets
Develop robust ETL/ELT processes using modern frameworks to support analytics and business intelligence use cases
Partner with product teams to:
Assemble complex data sets that meet both functional and non-functional requirements
Create Data Dictionaries, Data Catalogs, and documentation for metrics, KPIs, and data assets
Design and maintain data warehouses, data marts, and aggregations that enable insights into product performance, customer behavior, retention, and marketing effectiveness
Implement and maintain real-time dashboards and alerting systems for data monitoring and operational visibility
Who You Are Required Qualifications
1+ years of hands-on experience in Big Data and Data Engineering
Proficient in SQL and data warehouse design concepts, including DataMart modeling
Programming experience with Python, PySpark, or similar data languages
Experience developing and maintaining data pipelines in cloud or hybrid environments
Familiarity with at least one major cloud provider: GCP, AWS, or Azure
Experience working with relational and NoSQL databases (e.g., Postgres, MongoDB)
Hands-on experience with workflow orchestration tools such as:
Apache Airflow
Google Cloud Composer
AWS Data Pipeline
Experience using Git, Azure DevOps, or similar for version control and CI/CD
Familiarity with Docker and Kubernetes:
Building and deploying containers
Creating pods, config maps, and deployments using Terraform
Strong analytical, problem-solving, and communication skills
Ability to translate business requirements into technical documentation and data solutions
Bachelor's or advanced degree in Computer Science, Information Systems, or a related engineering discipline
Nice to Have
Exposure to Business Intelligence tools: Tableau, Power BI, Dundas, etc.
Experience working in Agile development environments
Previous work experience in cross-functional, distributed teams
