Our partner is a fast-growing B2B SaaS company focused on helping major e-commerce businesses optimize their growth potential. Their cutting-edge platform leverages advanced machine learning and data analytics to deliver speed and actionable insights that directly drive customer conversions and revenue growth for over a hundred brands, including Cozy Earth, Nood, and Glamnetic.
Backed by leading investors, including Crosscut Ventures and Signal Peak, their dynamic team is growing quickly and seeking a skilled Senior Data Engineer to join them in their mission and contribute to their continued success.
Role Overview:
As a Senior Data Engineer, you will play a crucial role in building and optimizing their data architecture to support their data-driven decision-making capabilities. Your expertise will drive the development of scalable data pipelines, enhance their data infrastructure, and ensure the integrity and accessibility of their data across various platforms. Additionally, you will be responsible for performing data modeling, analysis, and championing the effort to extract valuable insights and support strategic business decisions.
- Collaborate with different teams across the organization to define our data strategy.
- Work with Product and Engineering to develop the data models that power our customer insights and A/B testing systems.
- Design, develop, and maintain robust and scalable data pipelines to ingest, process, and store large volumes of data from many sources.
- Perform data modeling to structure and organize data for efficient analysis and business intelligence.
- Conduct data analysis to identify trends, patterns, and insights that inform business strategies.
- Optimize database performance and ensure the reliability, availability, and security of data platforms.
- Implement best practices for data governance, data quality, and data integration across the organization.
- Develop and maintain ETL processes to ensure timely and accurate data delivery.
- Troubleshoot and resolve data-related issues, ensuring data accuracy and consistency.
- Potentially engage in data science tasks, including developing and deploying machine learning models to enhance our platform’s capabilities.
- Stay current with emerging technologies and industry trends to continually improve data engineering practices.
- Strong communication skills and the ability to work independently as well as collaboratively with cross-functional teams.
- Proven experience in designing and implementing scalable data architectures.
- Ability to explain complex technical concepts to a non-technical audience.
- Excellent problem-solving skills and attention to detail.
- Ability to manage time effectively and work on multiple projects simultaneously.
- Lead the design and implementation of scalable data architectures and data integration solutions, ensuring they meet performance and security standards.
- Develop and manage ETL pipelines using orchestration tools (e.g. Airflow, Dagster) to ensure efficient data processing and delivery.
- Perform data modeling with dbt to create and maintain data warehouses and data lakes.
- Optimize database performance and manage data storage solutions.
- Implement data governance and data quality best practices.
- Troubleshoot data-related issues and ensure the integrity of data.
- Collaborate departmental stakeholders to ensure data solutions align with business goals.
- Mentor and guide junior data engineers and provide technical leadership to the team.
- Expert knowledge of SQL and data modeling techniques.
- Experience with big data technologies such as AWS Kinesis, Amazon Redshift, GCP Dataflow, Apache Beam, and/or BigQuery.
- Experience with orchestration tools such as Apache Airflow, Dagster
- Proficiency in programming languages such as Python.
- Strong understanding of data lake and warehousing concepts and technologies.
- Experience with cloud platforms like AWS, Google Cloud, or Azure.
- Excellent communication and collaboration skills.
- Proven track record of designing and implementing scalable data solutions.
- Experience with data analysis and data science tasks is a plus.
- Preferred: Certifications in AWS, Google Cloud, or relevant big data technologies.
