Senior Data Engineer Analyst (Mumbai)

PUMA Sports india; Mumbai City; On-site; INR 1,500,000 - 4,000,000; Full time

Job summary

Puma Energy is seeking an experienced Analyst - Data Engineer to design, develop, and maintain efficient data pipelines feeding the organization’s data lake. You will ensure data accuracy, timeliness, and quality to empower data scientists and business stakeholders for insights.

The role requires strong expertise in Azure Data Factory, Databricks, and data warehousing concepts, with experience in Python/PySpark and Scala.

Qualifications

• Bachelor's degree in Computer Science or related field required.
• 10+ years in data engineering/architecture roles preferred.
• Experience with Azure Data Factory, Databricks, and data lake platforms essential.

Responsibilities

• Design, develop, and maintain data pipelines and architecture.
• Collaborate with data scientists and stakeholders to ensure data quality.
• Build data models and catalog to support analytics across units.
• Implement scalable solutions using Azure cloud services.

Skills

Scala

.NET

Python

PySpark

Azure

Databricks

Azure Data Factory

Education

Bachelor's degree in Computer Science Engineering

Tools

Azure Data Factory

Azure Data Lake

Databricks

Hadoop

BigQuery

Job description

Analyst - Data Engineer Company Puma Energy is a leading global energy business that supplies, stores, and distributes petroleum products in 47 countries across six continents. Founded in 1997, Puma Energy has its headquarters in Singapore and Geneva and employs over 8,000 people globally with regional hubs in South Africa, Puerto Rico, and Estonia and has a Global Delivery Centre in Mumbai (India). Puma Energy has a network of 1948 retail sites and a presence at over 103 airports.

Our mission is to energize communities to help drive growth and prosperity by sustainably serving our customers needs in high-potential countries around the world Puma Processing Services LLP (PEPS) established in Year 2018, represents Puma Energys first captive offshoring unit and is based in Mumbai, India with over 200 employees. PEPS provides the Puma Group a key advantage of centralizing its core competency in services at one location and allows the group the ability to scale size to increase volumes in key areas. PEPS manages mid-office and back-office activities for Puma Businesses across all its business verticals like Retail, B2B, Aviation, and also enabling functions like IT and corporate.

It serves the countries that are a part of the African continent, Middle East, and Asia Pacific and further extends support to the Puma Group in Geneva for its core activities.

Main Purpose

Collaborate with data scientists and business stakeholders to design, develop, and maintain efficient data pipelines feeding into the organization's data lake. Ensure the data lake contains accurate, up-to-date, and high-quality data, enabling data scientists to develop insightful analytics and business stakeholders to make well-informed decisions. Utilize expertise in data engineering and cloud technologies to contribute to the overall success of the organization by providing the necessary data infrastructure and fostering a data-driven culture.

Demonstrate a strong architectural sense in defining data models, leveraging the Poly-base concept to optimize data storage and access. Facilitate seamless data integration and management across the organization, ensuring a robust and scalable data architecture. Take responsibility for defining and designing the data catalogue, effectively modelling all data within the organization, to enable productive data discovery, access, and management for various stakeholders.

Key Responsibilities

• Design, develop, optimize, and maintain data architecture and pipelines that adhere to ETL principles and business goals.
• Develop complex queries and solutions using Scala, .NET, Python/PySpark languages.
• Implement and maintain data solutions on Azure Data Factory, Azure Data Lake, and Databricks
• Create data products for analytics and data scientist team members to improve their productivity.
• Advise, consult, mentor, and coach other data and analytic professionals on data standards and practices.
• Foster a culture of sharing, re-use, design for scale stability, and operational efficiency of data and analytical solutions.
• Lead the evaluation, implementation, and deployment of emerging tools and processes for analytic data engineering in order to improve our productivity as a team.
• Develop and deliver communication and education plans on analytic data engineering capabilities, standards, and processes.
• Partner with business analysts and solutions architects to develop technical architectures for strategic enterprise projects and initiatives.
• Collaborate with other team members and effectively influence, direct, and monitor project work.
• Develop strong understanding of the business and support decision making.

Key Relationships

Internal CEO & COO of Africa Managers across various departments, Senior Management, Head of Departments in other regional hubs of Puma Energy External External Consultants

Work Experience

Work Experience: 10 years of overall experience & at least 5 years of relevant experience 5 years of experience working with Azure data factory & data bricks Skills : Bachelor's degree required; Computer Science Engineering. 5 years of experience working in data engineering or architecture role.

Experience developing and maintaining data warehouses in big data solutions.

Experience with, Azure Data Lake, Azure Data Factory, and Databricks) in the data and analytics space is a must Database development experience using Hadoop or Big Query and experience with a variety of relational, NoSQL, and cloud data lake technologies.

Worked with BI tools such as Tableau, Power BI, Looker, Shiny. Conceptual knowledge of data and analytics, such as dimensional modelling, ETL, reporting tools, data governance, data warehousing, and structured and unstructured data.

Big Data

Development experience using Hive, Impala, Spark, and familiarity with Kafka.

Exposure to machine learning, data science, computer vision .