Job Description

Job Summary (List Format):
- Design and implement scalable, end-to-end data solutions using cloud platforms (AWS, Azure, GCP), with a focus on AWS Redshift, Apache Airflow, dbt, and Databricks.
- Develop and maintain robust data pipelines for ingesting, transforming, and loading data from multiple sources into data warehouses.
- Create and orchestrate complex data workflows and ETL/ELT processes using Apache Airflow.
- Build and optimize data models for performance, scalability, cost-efficiency, and data quality using dbt and cloud data warehousing best practices.
- Support data analytics and business intelligence initiatives by enabling data access through dashboards and reports (primarily Tableau).
- Implement and enforce data governance, data quality, data lineage, data security, and compliance standards.
- Mentor junior engineers and promote best practices within the data engineering team.
- Evaluate, recommend, and adopt new data technologies to continuously improve data architecture.
- Collaborate cross-functionally with data analysts, data scientists, and business stakeholders.
- Apply strong programming skills (Python, Java, or Scala) and advanced SQL in data engineering tasks.
- Utilize distributed computing frameworks (e.g., Apache Spark, Hadoop) and containerization/orchestration tools (Docker, Kubernetes).
- Work within Agile project management methodologies.
- Hold a Bachelor's or Master's degree in Computer Science, Engineering, or a related field, with 5 7 years of relevant data engineering experience.

Skills Required