Data Engineer
Data Engineer
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Job Description
NPS Prism
Title: Data Engineer
Location: India (Hybrid)
Experience: 36 Years
Employment Type: Full-time
Company Profile:
NPS Prism is a market-leading cloud-based CX benchmarking and operational improvement platform owned by Bain & Company. NPS Prism provides its customers with actionable insights and analysis that guide the creation of game-changing customer experiences. Based on rock-solid sampling research and analytic methodology it lets customers see how they compare to their competitors on overall NPS and on every step of the customer journey.
With NPS Prism you can see where youre strong where you lag and how customers feel about doing business with you and your competitors in their own words. The result: Prioritize the customer interactions that matter most. NPS Prism customers use our customer experience benchmarks and insights to propel their growth and outpace the competition.
Launched in 2019 NPS Prism has rapidly grown to a team of over 200 serving dozens of clients around the world. NPS Prism is 100% owned by Bain & Company one of the top management consulting firms in the world and a company consistently recognized as one of the worlds best places to work. We believe that diversity inclusion and collaboration is key to building extraordinary teams. We hire people with exceptional talents abilities and potential then create an environment where you can become the best version of yourself and thrive both professionally and personally.
Position Summary:
We are seeking a highly skilled and experienced Data Engineer to join our team. The ideal candidate will have strong expertise in Python SQL and PySpark with proven experience working on Databricks and cloud platforms such as Azure and AWS. A solid understanding of ETL tools like Python as well as basic knowledge of DevOps practices and CI/CD pipelines will be advantageous. This is a unique opportunity to work in a dynamic and fast-paced environment to design and implement robust data solutions for scalable business needs. Working with Git and versioning.
Key Responsibilities:
Data Pipeline Development:
- Design build and optimize ETL/ELT workflows using tools like Databricks SQL Python/pyspark & Alteryx (Good to have).
- Develop and maintain robust scalable and efficient data pipelines for processing large datasets. from source to emerging data.
Cloud Data Engineering:
Work on cloud platforms (Azure AWS) to build and manage data lakes data warehouses and scalable data architectures.
Utilize cloud services like Azure Data Factory AWS Glue orfor data processing and orchestration.
Databricks and Big Data Solutions:
- Use Databricks for big data processing analytics and real-time data processing.
- Leverage Apache Spark for distributed computing and handling complex data transformations.
Data Management:
- Create and manage SQL-based data solutions ensuring high availability scalability and performance.
- Develop and enforce data quality checks and validation mechanisms.
Collaboration and Stakeholder Engagement:
- Collaborate with cross-functional teams including data scientists analysts and business stakeholders to deliver impactful data solutions.
- Understand business requirements and translate them into technical solutions.
DevOps and CI/CD:
Leverage CI/CD pipelines to streamline development testing and deployment of data engineering workflows.
- Work with DevOps tools like Git Jenkins or Azure DevOps for version control and automation.
Documentation and Optimization:
Maintain clear documentation for data workflows pipelines and processes.
Optimize data systems for performance scalability and cost-efficiency
Required Qualifications Experience and Skills
Educational Qualifications:
Bachelors or Masters degree in Computer Science Information Technology Engineering or a related field.
Experience:
36 years of experience in Data Engineering or related roles.
Hands-on experience with big data processing frameworks data lakes and cloud-native services.
Skills:
Core Skills:
- Proficiency in Python SQL and PySpark for data processing and manipulation.
- Proven experience in Databricks and Apache Spark.
- Expertise in working with cloud platforms like Azure AWS.
- Sound knowledge of ETL processes and tools like Alteryx. (Good to have)
Data Engineering Expertise:
Leveraging data lakes data warehouses and data pipelines.
Data Pipeline Build a Data Pipeline from scratch
Strong understanding of distributed systems and big data technologies.
DevOps and CI/CD:
Basic understanding of DevOps principles and familiarity with CI/CD pipelines.
Hands-on experience with tools like Git Jenkins or Azure DevOps.
Additional Skills:
Familiarity with data visualization tools like Power BI Tableau or similar is a plus.
Knowledge of streaming technologies such as Kafka or Event Hubs is desirable.
Strong problem-solving skills and a knack for optimizing data solutions.
Excellent communication (oral and written) skills
Required Experience:
Manager
Employment Type
Full-Time
