Data Engineer - Python/Apache Airflow (3-6 yrs)

Job Description : Data Engineer

- Role : Data Engineer

- Work Location : Bangalore (Work From Office)

- Experience : 3+ years

Job Overview :

We are looking for a proficient Data Engineer with expertise in Amazon Redshift, Python, Apache Airflow, dbt (Data Build Tool), API integration, and AWS. This role will be responsible for developing and maintaining scalable data pipelines, integrating data from multiple sources, and ensuring that our data architecture supports business intelligence, reporting, and analytics requirements. You will collaborate with cross-functional teams to build and optimize our data infrastructure and provide clean, high-quality data to the business.

Key Responsibilities :

- Data Pipeline Development : Build and maintain robust ETL/ELT pipelines using Python, Apache Airflow, and dbt to extract, transform, and load data from various sources into Amazon Redshift.

- Amazon Redshift Management : Design, optimize, and maintain Amazon Redshift clusters, ensuring the warehouse is capable of handling large-scale data efficiently.

- API Integration : Develop solutions to integrate external APIs for data ingestion, ensuring proper data extraction, transformation, and integration into our data infrastructure.

- Data Modeling : Create and maintain scalable data models in Redshift that support analytics and reporting needs, including designing star and snowflake schemas for optimized querying.

- AWS Infrastructure Management : Leverage AWS services such as S3, Lambda, EC2, and CloudWatch to build and maintain a scalable and cost-efficient data architecture.

- dbt (Data Build Tool) : Use dbt to manage and automate SQL transformations, ensuring modular, reusable, and well-documented data transformation logic.

- Workflow Orchestration : Utilize Apache Airflow to orchestrate and automate data workflows, ensuring reliable data pipelines and scheduled jobs.

- Data Quality & Testing : Implement and maintain data validation checks and testing frameworks to ensure data integrity, accuracy, and compliance across all data pipelines.

- Collaboration : Work closely with data scientists, analysts, and product teams to understand data needs and provide technical solutions that meet business objectives.

- Performance Optimization : Tune SQL queries and manage the performance of Redshift clusters to ensure fast, efficient data access and analysis.

- Data Governance : Enforce data governance policies to ensure compliance with security, privacy, and data quality standards throughout the data lifecycle.

Key Skills & Qualifications :

- Bachelors/Masters degree in Computer Science, Engineering, Data Science, or a related field.

- 3+ years of experience in data engineering with expertise in Amazon Redshift, Python, and AWS.

- Strong experience with Apache Airflow for workflow scheduling and orchestration.

- Hands-on experience with dbt (Data Build Tool) for managing SQL transformations and data models.

- Proficiency in API development and integration, including the use of RESTful APIs for data ingestion.

- Extensive experience with AWS services such as S3, Lambda, EC2, RDS, and CloudWatch.

- Expertise in data modeling concepts and designing efficient data structures (e.g., star schemas, snowflake schemas) in a data warehouse environment.

- Advanced knowledge of SQL for querying and optimizing large datasets in Redshift.

- Experience building ETL/ELT pipelines and integrating data from multiple sources, including structured and unstructured data.

- Familiarity with version control systems like Git and best practices for code management and deployment automation.

- Knowledge of data governance principles, including data security, privacy, and quality control.

Preferred Qualifications :

- Experience with real-time data processing tools such as Kafka or Kinesis.

- Familiarity with data visualization tools like Tableau, Looker, or Power BI.

- Knowledge of other data warehousing solutions like Snowflake or Google BigQuery.

- Experience with DevOps practices for managing infrastructure and CI/CD pipelines (Docker, Kubernetes).

- Understanding of machine learning pipelines and how data engineering supports AI/ML initiatives.

Soft Skills :

- Strong analytical and problem-solving skills.

- Ability to work independently and as part of a cross-functional team.

- Strong written and verbal communication skills, with the ability to explain technical concepts to non- technical stakeholders.

- Detail-oriented, proactive, and self-motivated with a focus on continuous improvement.

- Strong organizational and project management skills to handle multiple tasks in a fast-paced environment.