Lead Data Engineer

Lead Data Engineer

Company :

We are a leading impact data, analytics and product solutions provider that has been measuring, quantifying and valuing corporate impacts for more than 16 years.

With a team of 100+ scientists, engineers, data scientists and environmental economists, We deliver market-leading impact platforms and datasets, covering 14,000+ companies with geographically precise, time-series data.

We work with pioneering companies across all sectors and with investors representing over $8 trillion in assets under management.

We also partner with some of the world's largest ESG data providers, business networks, and fintech platforms to enable impact measurement across global markets.

We are at the cutting edge of measuring impacts using a scientifically sound and economically rigorous methodology that provides unique and unparalleled insight into how companies impact society and the environment. In addition, our leadership team comprises product, technology and commercial leaders who have worked across Meta, Adobe, AT Kearney, Bain & Co., McKinsey & Co., Monitor Deloitte, etc.

Role Description :

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.

- Strong communication and interpersonal skills.

- Proven ability to work collaboratively in a fast-paced, dynamic environment.