Data Engineer

1 week ago

Navi Mumbai, Maharashtra, India talent leads Full time

Job Description :


Key Responsibilities :

Manage Data Warehouse/Datalake Implementation :

- Lead the end-to-end implementation of Data Warehouse/Datalake projects.

- Ensure adherence to project timelines and quality standards.

Collaborate on Data Modeling and ETL Processes :

- Work closely with the development team to design and implement effective data models.

- Develop robust ETL processes to collect, process, and store structured and unstructured data.

Support Analytics and Reporting Initiatives :

- Build and optimize data warehouses, data lakes, and storage solutions.

- Enable analytics, reporting, and machine learning by providing reliable data infrastructure.

Data Infrastructure Development and Maintenance :

- Develop and maintain data infrastructure and tools.

- Implement automation for data processes and adhere to best practices in data management, governance, and security.

Performance Optimization and Issue Resolution :

- Troubleshoot issues and optimize the performance of data systems.

- Identify bottlenecks and implement solutions to meet business performance goals.

Stakeholder Collaboration and Data Quality Assurance :

- Collaborate with data scientists, analysts, and stakeholders to understand data requirements.

- Ensure data quality, integrity, and availability throughout the data lifecycle.

Technical Skills :

- Programming Languages : Proficiency in languages such as Python, SQL, and optionally Java or Scala for implementing data pipelines and ETL processes.

- Data Warehousing : Experience with designing and optimizing data warehouses using technologies like Amazon Redshift, Google BigQuery, or Snowflake.

- Big Data Technologies : Familiarity with Hadoop ecosystem components (HDFS, Hive, Spark) or similar distributed computing frameworks for processing large-scale data.

- ETL Tools : Hands-on experience with ETL tools like Apache Airflow, Apache NiFi, Talend, or Informatica for orchestrating data workflows.

- Database Management: Strong understanding of relational databases (e., PostgreSQL, MySQL) and NoSQL databases (e., MongoDB, Cassandra) for data storage and retrieval.

Qualifications :

Education :


- A degree in Computer Science, Engineering, Information Systems, or a related field.

- A Master's degree or relevant certifications (e., AWS Certified Big Data - Specialty, Google Professional Data Engineer) can be advantageous.

- Experience : Typically, 2+ years of experience in data engineering or a related role, with a proven track record of designing and implementing data pipelines and data warehouses.

- Analytical Skills : Strong analytical and problem-solving skills to troubleshoot issues, identify bottlenecks, and optimize data processes.

- Communication : Excellent communication skills to collaborate effectively with cross-functional teams including data scientists, analysts, and business stakeholders

(ref:hirist.tech)

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