Senior Data Engineer

About Saras Analytics:

Where we are Today:

We are a boot strapped, profitable & fast growing (2x y-o-y) startup
with old school value systems. We play in a very exciting space which is
intersection of data analytics & ecommerce both of which are game changers.
Today, the global economy faces headwinds forcing companies to downsize,
outsource & offshore creating strong tail winds for us. We are an employee
first company valuing talent & encouraging talent and live by those values
at all stages of our work without comprising on the value we create for our
customers. We strive to make Saras a career and not a job for talented folks
who have chosen to work with us.

The Role:

We are
seeking a seasoned and proficient Senior Python Data Engineer with substantial
experience in cloud technologies. As a pivotal member of our data engineering
team, you will play a crucial role in designing, implementing, and optimizing
data pipelines, ensuring seamless integration with cloud platforms. The ideal
candidate will possess a strong command of Python, data engineering principles,
and a proven track record of successful implementation of scalable solutions in
cloud environments.

Responsibilities:

1.      Data Pipeline Development:

·       Design, develop, and maintain scalable and efficient data pipelines
using Python and cloud-based technologies.

·       Implement Extract, Transform, Load (ETL) processes to seamlessly move
data from diverse sources into our cloud-based data warehouse.

2.      Cloud Integration:

·       Utilize cloud platforms (e.g., Google Cloud, AWS, Azure) to deploy,
manage, and optimize data engineering solutions.

·       Leverage cloud-native services for storage, processing, and analysis of
large datasets.

3.      Data Modelling and Architecture:

·       Collaborate with data scientists, analysts, and other stakeholders to
design effective data models that align with business requirements.

·       Ensure the scalability, reliability, and performance of the overall data
infrastructure on cloud platforms.

4.      Optimization and Performance:

·       Continuously optimize data processes for improved performance,
scalability, and cost-effectiveness in a cloud environment.

·       Monitor and troubleshoot issues, ensuring timely resolution and minimal
impact on data availability.

5.      Quality Assurance:

·       Implement data quality checks and validation processes to ensure the
accuracy and completeness of data in the cloud-based data warehouse.

·       Collaborate with cross-functional teams to identify and address data
quality issues.

6.      Collaboration and Communication:

·       Work closely with data scientists, analysts, and other teams to
understand data requirements and provide technical support.

·       Collaborate with other engineering teams to seamlessly integrate data
engineering solutions into larger cloud-based systems.

7.      Documentation:

·       Create and maintain comprehensive documentation for data engineering
processes, cloud architecture, and pipelines.

Technical Skills:

1.        Programming Languages: Proficiency
in Python for data engineering tasks, scripting, and automation.

2.       Data Engineering Technologies:

·       Extensive experience with data engineering frameworks like distributed
data processing.

·       Understanding and hands-on experience with workflow management tools
like Apache Airflow.

3.        Cloud Platforms:

·       In-depth knowledge and hands-on experience with at least one major cloud
platform: AWS, Azure, or Google Cloud. 

·       Familiarity with cloud-native services for data processing, storage, and analytics. 

4.       ETL Processes: Proven
expertise in designing and implementing Extract, Transform, Load (ETL)
processes.

5.       SQL and Databases: Proficient
in SQL with experience in working with relational databases (e.g., PostgreSQL,
MySQL) and cloud-based database services.

6.       Data Modeling: Strong
understanding of data modeling principles and experience in designing effective
data models.

7.       Version Control: Familiarity
with version control systems, such as Git, for tracking changes in code and
configurations.

8.       Collaboration Tools: Experience
using collaboration and project management tools for effective communication
and project tracking.

9.       Containerization and Orchestration: Familiarity
with containerization technologies (e.g., Docker) and orchestration tools
(e.g., Kubernetes).

10.     Monitoring and Troubleshooting: Ability
to implement monitoring solutions and troubleshoot issues in data pipelines.

11.     Data Quality Assurance: Experience
in implementing data quality checks and validation processes.

12.     Agile Methodologies: Familiarity
with agile development methodologies and practices.

Soft Skills:

• Strong problem-solving
  and critical-thinking abilities.
  
• Excellent
  communication skills, both written and verbal.
  
• Ability to work
  collaboratively in a cross-functional team environment.
  
• Attention to detail
  and commitment to delivering high-quality solutions.
  

If you
possess the required technical skills and are passionate about leveraging cloud
technologies for data engineering, we encourage you to apply. Please submit
your resume and a cover letter highlighting your technical expertise and
relevant experience.