Ganit - VP/AVP- Data Engineering

An exceptional individual to lead and manage a team of data engineers and analytics engineers who design, build and maintain the data infrastructure and platform for all our projects and products.

1. Design and develop software components encompassing the complete data lifecycle, including data acquisition, transformation, management, export, and analytics.

2. Create a secure, extensible, and reliable platform for consuming, integrating, and analyzing complex data using leading open-source tools and platforms.

3. Architect the data infrastructure to support large-scale data science projects and drive innovation in data processing.

4. Develop software solutions to enable enterprise-level delivery of industry-leading machine learning insights.

5. Build APIs and interfaces to deliver key analytical and data science insights to stakeholders.

6. Identify opportunities for data-driven applications that enhance efficiency through automation and machine learning.

7. Collaborate with cross-functional teams and Data Subject Matter Experts to interpret requirements and drive successful solutions.

8. Drive the strategic information management roadmap, covering advanced analytics, big data, and cloud-based platforms.

9. Evaluate and analyze the latest Big Data Analytic technologies for continuous improvement in data engineering and processing.

10. Collaborate with IT teams to establish Big Data reference architecture patterns for data ingestion, processing, analytics, and data science solutions.

Profile requirement:

1. Experience Building Stream-Processing Applications: Hands-on experience in creating stream-processing applications using technologies like Apache Flink, Spark Streaming, Apache Storm, Kafka Streams, or others.

2. Database Experience: Proficiency in working with both NoSQL databases and relational databases such as MySQL and Postgres, as well as analytics databases like Druid or Apache Pinot.

3. Cloud Computing Platforms: Experience with major cloud computing platforms such as Amazon AWS, Google Cloud, Microsoft Azure.

4. Containers and CI/CD: Familiarity with containerization technologies like Docker and orchestration with Kubernetes, along with knowledge of CI/CD tools like Jenkins.

5. Data Lake/Warehouse Architectures: Thorough understanding of data lake and data warehouse architectures, including platforms like BigQuery, Snowflake, and RedShift.

6. Strong Software Engineering Experience: Significant software engineering experience and proficiency in at least one high-level programming language such as Java, Scala, Python, or equivalent.

7. Education: A graduate or postgraduate degree in computer science, engineering, mathematics, statistics, or a related field, or equivalent practical experience in data engineering.

8. Domain Expertise in Retail/CPG Industries: Expert knowledge of Big Data and data warehouse concepts in the Retail/Consumer Packaged Goods (CPG) industries.

LOCATION : MUMBAI / BANGALORE / CHENNAI