Data Engineer

Hello all,

We are happy to assist the product and service company “Saras Analytics” in creating new multinational remote team. We are open to meet people who are able to work 8 hours between 2 p.m. to 10 p.m. CET, as a contractors.

Now, a little bit about the company and the product:

Saras Analytics is a rapidly growing data management and analytics advisory firm with offices in Austin, USA and Hyderabad, India. We are a group of engineers and analysts focused on accelerating growth for e-commerce and digital businesses by setting up or transforming their data (analytics & BI) ecosystems and providing further analytics services. We are laser focused on providing the best ROI for our clients and leave no stone unturned in our quest to provide the best results for our customers.

We are an employee-centric organization and, to meet the ever-growing demand for our services, are looking for individuals who share our passion to make a difference and would be great additions to our analytics and growth consulting practice.

How Saras Analytics describes your role:

As a Data Engineer at Saras Analytics, you will be responsible for building and maintaining large-scale data pipelines as well as create and data pipelines that deal with large volumes of data. You will bring your expertise in Python or PySpark, and it's good to have experience with DBT (Data Build Tool). As a Data Engineer, you will play a crucial role in designing, building, and maintaining our data infrastructure in the cloud. You will collaborate with cross-functional teams to ensure data is collected, processed, and made available for analysis and reporting. If you are passionate about data engineering, cloud technologies, and have strong programming skills, we invite you to apply for this exciting position.

You will deal with:

1. Data Pipeline Development: Design, develop, and maintain scalable data pipelines that collect, process, and transform data from various sources into usable formats for analysis and reporting.
2. Cloud Integration: Leverage cloud platforms such as AWS, Azure, or Google Cloud to build and optimize data solutions, ensuring efficient data storage, access, and security.
3. Python/PySpark Expertise: Utilize Python and/or PySpark for data transformation, manipulation, and ETL processes. Write clean, efficient, and maintainable code.
4. Data Modeling: Create and maintain data models that align with business requirements, ensuring data accuracy, consistency, and reliability.
5. Data Quality: Implement data quality checks and validation processes to ensure the integrity of the data, troubleshooting and resolving issues as they arise.
6. Performance Optimization: Identify and implement performance optimizations in data pipelines and queries to ensure fast and efficient data processing.
7. Collaboration: Collaborate with data scientists, analysts, and other stakeholders to understand their data requirements and provide them with reliable data sets.
8. Documentation: Maintain thorough documentation of data pipelines, workflows, and processes to ensure knowledge sharing and team efficiency.
9. Security and Compliance: Implement security best practices and ensure data compliance with relevant regulations and company policies.

Good to Have (Preferred Skills):

• DBT (Data Build Tool): Experience with DBT for managing and orchestrating data transformations.
• Containerization and Orchestration: Experience with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Data Streaming: Knowledge of data streaming technologies (e.g., Kafka, Apache Spark Streaming).
• Workflow Management: Familiarity with data orchestration and workflow management tools (e.g., Apache Airflow).
• Cloud Certification: Certification in cloud services (e.g., AWS Certified Data Analytics, Azure Data Engineer).
• Data Governance: Understanding of data governance and data cataloging.

What you bring with you:

• Bachelor's degree in Computer Science, Information Technology, or a related field (Master's degree preferred).
• Proven experience as a Data Engineer, with a focus on cloud-based solutions.
• Strong proficiency in Python and/or PySpark for data processing and ETL tasks.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Knowledge of data warehousing concepts and technologies (e.g., Redshift, BigQuery).
• Familiarity with data modelling and database design principles.
• Solid understanding of data integration and ETL best practices.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills to work effectively in cross-functional teams.

Eligibility:

Significant technical academic course work or equivalent work experience.

Excellent communication and interpersonal skills.

Dedicate 40 hours/weekly to Saras Analytics.

The recruitment process:

1. HR interview.
2. Technical interview.

Let’s connect and check if we match!

You can state your interest by sending your CV and we will get in touch with the short-listed candidates.

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