Data Modeller

Responsibilities :

 Analyze and organize raw data: Work with various data sources, document parsing,

extracting relevant information and structuring it for further processing.

 Build data systems and pipelines: Construct robust data pipelines that facilitate data

flow from source to Target.

 Evaluate business needs and objectives: Understand the company's requirements

and align data systems accordingly.

 Interpret trends and patterns: Use your analytical skills to identify data patterns.

 Conduct complex data analysis and report on results: Dive deep into data to extract

meaningful information.

 Prepare data for prescriptive and predictive modeling: Ensure data is ready for

machine learning and statistical analysis.

 Build algorithms and prototypes: Develop and test data processing algorithms.

 Combine raw information from different sources: Integrate data from various systems.

 Explore ways to enhance data quality and reliability: Continuously improve data

processes.

 Identify opportunities for data acquisition: Stay informed about new data sources.

 Develop analytical tools and programs: Create tools to facilitate data analysis.

 Collaborate with data scientists and architects: Work closely with other data

professionals to achieve common goals.

 Implement data access controls, data encryption, and data masking techniques

 Familiarity with data visualization tools and techniques for presenting data

 Create and maintain dashboards and reports for stakeholders.

Mandatory Skills :

 Previous experience as a data engineer or in a similar role with at least 6+ years of

relevant work experience.

 Good knowledge of programming languages (e.g., Python, Java, Spark, etc).

 Design, develop, and maintain data pipelines.

 Exposure to process automation.

 Experience working with REST APIs and services, messaging and event technologies.

 Experience working with large and complex data sets.

 Hands-on experience with SQL/No-SQL database (RDS, Redshift, DynamoDB,

synapse, big query, mongo, etc.)

 Batch/stream data processing experience

 Monitor, troubleshoot, and optimize the performance of data infrastructure to ensure

scalability, reliability, and cost efficiency.

 Stay up to date with cloud services and best practices in data engineering to

continuously improve our data ecosystem.

 Good exposure on at least two public cloud platforms (Azure/AWS/GCP)

 Experience with Graph database (e.g. Neptune, RDF4j, etc.)

 Experience with Vector database (e.g. Pinecone, FAISS, etc.)

Good-to-Have Skills :

- knowledge or work experience in insurance, mortgage, banking domains.

- Proficiency in building stream processing systems using kinesis, Kafka, etc.

- Familiarity with Docker, Kubernetes, CI/CD and cloud services (AWS, Azure, GCP).

- Technical expertise with segmentation techniques.

- NLP knowledge