Startup and product based companies experience is a must.
You are available to join within 15-20 days.
Tech from Tier I/II institute is a must.
Basic Qualifications
Bachelors Degree in Computer Science, Engineering, or a related technical field from a Tier-I/II institution.
Proven experience as a Data Engineer with expertise in ETL techniques (3+ years) is a MUST.
Strong programming skills in languages such as Python, Java, or Scala.
Skillset to scrape and transform data off of the publicly available web sources.
Experience with cloud-based data platforms (e.g., AWS, Azure, GCP).
Proficiency in SQL and experience working with relational and non-relational databases.
Knowledge of data warehousing concepts and architectures.
Familiarity with big data technologies such as Hadoop, Spark, and Kafka.
Experience with data modeling tools and techniques.
Excellent problem-solving and analytical skills.
Strong communication and collaboration skills.
Preferred Qualifications
Master's degree or equivalent in Computer Science/ Data Science.
Knowledge of data streaming and real-time processing.
Familiarity with data governance and security best practices.
Key Responsibilities
ETL Development :
Design, develop, and maintain efficient ETL processes for multi-scale data sets.
Implement and optimize data transformation and validation processes to ensure data accuracy and consistency.
Collaborate with cross-functional teams to understand data requirements and business logic.
Data Pipeline Architecture
Architect, build and maintain scalable and high-performance data pipelines.
Evaluate and implement new technologies to enhance data pipeline efficiency and reliability.
Pipelines for extracting data through scraping for ad-hoc sector specific Modelling :
Develop and implement data models to support analytics and reporting needs.
Optimize database structures for performance and Quality and Governance :
Implement data quality checks and governance processes to ensure data integrity.
Collaborate with stakeholders to define and enforce data quality and Communication :
Document ETL processes, data models, and other relevant information.
Communicate complex technical concepts to non-technical stakeholders collaboration :
Collaborate internally with the Quant team and developers to lay and optimize the data pipelines and externally with the stakeholders to understand the business requirements for the enrichment of the cloud database.
(ref:hirist.tech)
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