Data Engineer

Project Role : Data Engineer

Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems.

Must have skills : Data Engineering, Data Architecture Principles, Databricks Unified Data Analytics Platform, Microsoft Azure Data Services

Good to have skills : NA

Minimum 3 year(s) of experience is required

Educational Qualification : 15 years full time education

Summary:

As a Data Engineer, a typical day involves designing, developing, and maintaining comprehensive data solutions that support the generation, collection, and processing of data. This role requires creating efficient data pipelines and ensuring the integrity and quality of data throughout its lifecycle. The position also involves implementing processes to extract, transform, and load data, facilitating seamless migration and deployment across various systems. Collaboration with different teams to align data strategies and optimize workflows is an integral part of daily activities, ensuring that data infrastructure supports organizational needs effectively and reliably. Utilizes software engineering principles to deploy and maintain fully automated data transformation pipelines that combine a large variety of storage and computation technologies to handle a distribution of data types and volumes in support of data architecture design. A Data Engineer designs data products and data pipelines that are resilient to change, modular, flexible, scalable, reusable, and cost effective.

Roles & Responsibilities:

- Design, develop, and maintain data pipelines and ETL processes using Microsoft Azure services (e.g., Azure Data Factory, Azure Synapse, Azure Databricks, Azure Fabric)

- Utilize Azure data storage accounts for organizing and maintaining data pipeline outputs. (e.g., Azure Data Lake Storage Gen 2 & Azure Blob storage)

- Collaborate with data scientists, data analysts, data architects and other stakeholders to understand data requirements and deliver high-quality data solutions

- Optimize data pipelines in the Azure environment for performance, scalability, and reliability

- Ensure data quality and integrity through data validation techniques and frameworks

- Develop and maintain documentation for data processes, configurations, and best practices

- Monitor and troubleshoot data pipeline issues to ensure timely resolution

- Stay current with industry trends and emerging technologies to ensure our data solutions remain cutting-edge

- Manage the CI/CD process for deploying and maintaining data solutions.

- Expected to be an SME, collaborate and manage the team to perform.

- Responsible for team decisions.

- Engage with multiple teams and contribute on key decisions.

- Provide solutions to problems for their immediate team and across multiple teams.

- Lead the design and implementation of scalable data architectures to support business requirements.

- Oversee the continuous improvement of data processing workflows to enhance efficiency and reliability.

- Mentor junior team members to foster skill development and knowledge sharing within the team.

Professional & Technical Skills:

- Must To Have Skills: Proficiency in Data Engineering, Data Architecture Principles, Databricks Unified Data Analytics Platform, Microsoft Azure Data Services.

- Strong experience in building and optimizing data pipelines and ETL frameworks.

- In-depth knowledge of cloud-based data storage and processing solutions.

- Ability to work with large datasets and ensure data quality and consistency.

- Familiarity with scripting and programming languages commonly used in data engineering.

- Experience in monitoring and troubleshooting data workflows to maintain system performance.

- Learning agility

- Technical Leadership

- Consulting and managing business needs

- Strong experience in Python is preferred but experience in other languages such as Scala, Java, C#, etc is accepted

- Experience building spark applications utilizing PySpark.

Additional Information:

- The candidate should have minimum 5 years of experience in Data Engineering.

- This position is based at our Pune office.

- A 15 years full time education is required.15 years full time education

About Accenture

Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services-creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

Visit us atwww.accenture.com

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We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, militaryveteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicablelaw. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.