Data Engineer

Key Responsibilities: Data Pipeline Development : Design, build, and optimize scalable ETL/ELT pipelines to process and transform data from various sources into Snowflake. Data Modeling : Build and maintain data models within Snowflake for optimized querying and reporting. Cloud Infrastructure : Leverage AWS services (e.g., S3, Redshift, Lambda, Glue) for data storage, processing, and orchestration.

Automation & Infrastructure as Code : Use Terraform to automate and manage cloud infrastructure deployments and ensure scalability, reliability, and efficiency. Reporting & Visualization : Collaborate with BI teams to integrate data with Tableau for reporting, dashboards, and analytics. Data Quality & Governance : Implement best practices for data quality, governance, and security in line with company policies.

Performance Optimization : Continuously monitor and improve the performance of data systems and pipelines, ensuring low-latency and high-availability. Collaboration : Work closely with cross-functional teams (data scientists, analysts, product managers) to deliver actionable insights and products. Troubleshooting & Support : Provide ongoing support to ensure that data systems and pipelines are running smoothly and addressing issues as they arise.

Skills & Qualifications: Experience with Snowflake : Proficiency in Snowflake for data warehousing, including data loading, transformation, and optimization. AWS Expertise : Hands-on experience with AWS tools such as S3, Redshift, Lambda, Glue, and others for data processing and storage. Data Pipeline Development : Experience building and maintaining end-to-end data pipelines using tools like Apache Airflow, DBT, or similar.

Tableau : Solid experience in integrating and visualizing data in Tableau for reporting and dashboard creation. Terraform : Experience in Infrastructure as Code (IaC) using Terraform to manage cloud resources. SQL Proficiency : Strong SQL skills for data querying, transformation, and troubleshooting.

Programming : Familiarity with Python or other programming languages for building custom data pipelines and automation. Data Governance & Security : Understanding of data governance principles, security best practices, and compliance requirements. Communication : Strong communication skills to collaborate with technical and non-technical teams.

Nice to Have: Experience with containerization (Docker, Kubernetes). Knowledge of machine learning models and integration into data pipelines. Agile or Scrum methodology experience.

Familiarity with CI/CD processes for data engineering workflows.