Build, maintain, and optimize scalable batch and real-time data pipelines using PySpark, Spark, and cloud platforms. Develop ETL/ELT processes, data ingestion strategies, data lakes, and Big Data frameworks. Support data platform architecture, governance, security, performance tuning, and deployment across AWS, Azure, or GCP. Collaborate with data scientists, analysts, architects, and engineers to ensure reliable, secure, and accessible data.
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 : Databricks Unified Data Analytics Platform
Good to have skills : NA
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a Data Platform Engineer, you will assist with the data platform blueprint and design, collaborating with Integration Architects and Data Architects to ensure cohesive integration between systems and data models. You will play a crucial role in shaping the data platform components.
Roles & Responsibilities:
- Work as part of the data engineering team to build, maintain, and optimize scalable data pipelines for large-scale data processing.
Develop and implement ETL/ELT processes using PySpark, Spark, and other relevant tools to move and transform data from various sources.
Assist in designing and deploying solutions in major cloud platforms such as AWS, Azure, or GCP.
Support the development and maintenance of Big Data processing frameworks and data lakes to handle structured and unstructured data.
Collaborate with data scientists, analysts, and other engineers to ensure data accuracy and availability.
Implement data ingestion strategies, ensuring the secure and efficient movement of data across different storage solutions.
Work on real-time streaming data pipelines and batch data processing to handle high-volume workloads.
Develop and maintain reusable code for data extraction, transformation, and loading (ETL) operations.
Contribute to performance tuning of Spark jobs and data pipelines to ensure scalability and efficiency.
Assist in maintaining governance and data security practices across cloud platforms.
Professional & Technical Skills:
- Experience with AWS, Azure, or GCP for data engineering workflows.
Strong proficiency in PySpark, Spark, or similar frameworks for building scalable data pipelines.
Understanding of Big Data architectures, data storage, and data processing concepts.
Familiarity with cloud-native data storage solutions such as S3, Blob Storage, BigQuery, or Redshift.
Experience with data orchestration tools like Apache Airflow or similar.
Knowledge of data formats like Parquet, Avro, or JSON.
Strong coding skills in Python for building data pipelines.
Good understanding of SQL and database technologies.
Excellent troubleshooting, debugging, and performance optimization skills
Additional Information:
- Experience with real-time data processing and Kafka or similar tools.
Exposure to CI/CD pipelines and DevOps practices in cloud environments.
Familiarity with dbt (Data Build Tool) for data transformation workflows.
Experience with other ETL tools like Informatica, Talend, or Matillion
Educational Qualification:
- 15 years full time education is required.
15 years full time education
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 : Databricks Unified Data Analytics Platform
Good to have skills : NA
Minimum 3 year(s) of experience is required
Educational Qualification : 15 years full time education
Summary:
As a Data Platform Engineer, you will assist with the data platform blueprint and design, collaborating with Integration Architects and Data Architects to ensure cohesive integration between systems and data models. You will play a crucial role in shaping the data platform components.
Roles & Responsibilities:
- Work as part of the data engineering team to build, maintain, and optimize scalable data pipelines for large-scale data processing.
Develop and implement ETL/ELT processes using PySpark, Spark, and other relevant tools to move and transform data from various sources.
Assist in designing and deploying solutions in major cloud platforms such as AWS, Azure, or GCP.
Support the development and maintenance of Big Data processing frameworks and data lakes to handle structured and unstructured data.
Collaborate with data scientists, analysts, and other engineers to ensure data accuracy and availability.
Implement data ingestion strategies, ensuring the secure and efficient movement of data across different storage solutions.
Work on real-time streaming data pipelines and batch data processing to handle high-volume workloads.
Develop and maintain reusable code for data extraction, transformation, and loading (ETL) operations.
Contribute to performance tuning of Spark jobs and data pipelines to ensure scalability and efficiency.
Assist in maintaining governance and data security practices across cloud platforms.
Professional & Technical Skills:
- Experience with AWS, Azure, or GCP for data engineering workflows.
Strong proficiency in PySpark, Spark, or similar frameworks for building scalable data pipelines.
Understanding of Big Data architectures, data storage, and data processing concepts.
Familiarity with cloud-native data storage solutions such as S3, Blob Storage, BigQuery, or Redshift.
Experience with data orchestration tools like Apache Airflow or similar.
Knowledge of data formats like Parquet, Avro, or JSON.
Strong coding skills in Python for building data pipelines.
Good understanding of SQL and database technologies.
Excellent troubleshooting, debugging, and performance optimization skills
Additional Information:
- Experience with real-time data processing and Kafka or similar tools.
Exposure to CI/CD pipelines and DevOps practices in cloud environments.
Familiarity with dbt (Data Build Tool) for data transformation workflows.
Experience with other ETL tools like Informatica, Talend, or Matillion
Educational Qualification:
- 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 at www.accenture.com
Equal Employment Opportunity Statement
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, military veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
Accenture Chennai, Tamil Nadu, IND Office
Chennai, India
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What you need to know about the Chennai Tech Scene
To locals, it's no secret that South India is leading the charge in big data infrastructure. While the environmental impact of data centers has long been a concern, emerging hubs like Chennai are favored by companies seeking ready access to renewable energy resources, which provide more sustainable and cost-effective solutions. As a result, Chennai, along with neighboring Bengaluru and Hyderabad, is poised for significant growth, with a projected 65 percent increase in data center capacity over the next decade.
