As a Data Engineering Lead, you will play a crucial role in overseeing the design, development, and maintenance of our organization's data architecture and infrastructure. You will be responsible for designing and developing the architecture for the data platform that ensures the efficient and effective processing of large volumes of data, enabling the business to make informed decisions based on reliable and high-quality data.

The ideal candidate will have a strong background in data engineering, excellent leadership skills, and
a proven track record of successfully managing complex data projects.

Responsibilities :

Data Architecture and Design :

- Design and implement scalable and efficient data architectures to support the organization's data
processing needs

- Work closely with cross-functional teams to understand data requirements and ensure that data
solutions align with business objectives

ETL Development:

- Oversee the development of robust ETL processes to extract, transform, and load data from
various sources into the data warehouse

- Ensure data quality and integrity throughout the ETL process, implementing best practices for
data cleansing and validation

Big Data Technologies:

- Stay abreast of emerging trends and technologies in big data and analytics, and assess their
applicability to the organization's data strategy

- Implement and optimize big data technologies to process and analyze large datasets efficiently

Cloud Integration:

- Collaborate with the IT infrastructure team to integrate data engineering solutions with cloud
platforms, ensuring scalability, security, and performance

- Performance Monitoring and Optimization:

- Implement monitoring tools and processes to track the performance of data pipelines and
proactively address any issues

- Optimize data processing workflows for improved efficiency and resource utilization

Documentation:

- Maintain comprehensive documentation for data engineering processes, data models, and
system architecture

- Ensure that team members follow documentation standards and best practices.

Collaboration and Communication :

- Collaborate with data scientists, analysts, and other stakeholders to understand their data needs
and deliver solutions that meet those requirements

- Communicate effectively with technical and non-technical stakeholders, providing updates on
project status, challenges, and opportunities.

Qualifications :

- Bachelor's or Master's degree in Computer Science, Information Technology, or a related field.

- 6-8 years of professional experience in data engineering

- In-depth knowledge of data modeling, ETL processes, and data warehousing.

- In-depth knowledge of building the data warehouse using Snowflake

- Should have experience in data ingestion, data lakes, data mesh and data governance

- Must have experience in Python programming

- Strong understanding of big data technologies and frameworks, such as Hadoop, Spark, and Kafka.

- Experience with cloud platforms, such as AWS, Azure, or Google Cloud.

- Familiarity with database systems like SQL, NoSQL, and data pipeline orchestration tools.

- Excellent problem-solving and analytical skills.