About us:
We are a cross commerce digital platform that allows you to source your food products, providing a seamless purchase experience and year-round availability. We are on a mission to alleviate one of humanity’s most significant challenges: food security.
The role:
● Collect and use market data, enabling the company to stay competitive and informed on industry and market trends.
● Optimize operations and automate processes, improving efficiency and agility.
● Leverage data for competitive advantage through product innovation and new revenue streams.
● Align data initiatives with business goals, ensuring data efforts directly impact growth and strategy.
● Generate actionable insights to drive customer acquisition, product development, and market strategy.
● Establish a data-driven culture from the beginning, promoting informed decision-making.
● Enable rapid experimentation and prototyping, ensuring quick validation of business ideas.
● Centralize data efforts, avoiding silos and ensuring data is accessible to all teams.
● Ensure compliance with data privacy and security regulations to mitigate risks.
● Prepare for future scaling needs by building a robust data team and infrastructure.
● Build scalable data infrastructure to support future growth and prevent technical debt.
What would the goals and main functions of the Data Leader be?
Goals of a Data Leader:
● Maximize Data Value: Transform raw data into actionable insights that align with the company’s strategic goals and improve overall performance.
● Enable Data-Driven Decision Making: Foster a culture of data-driven decision-making at all levels of the organization.
● Ensure Data Quality and Integrity: Oversee date governance frameworks to ensure the accuracy, consistency, and security of the data.
● Drive Innovation: Utilize data to identify new opportunities, create data products, and leverage emerging technologies like Al and machine learning.
● Optimize Data Infrastructure: Ensure that the organization’s data architecture, tools, and technologies support efficient data processing, analysis, and storage.
● Mitigate Risks: Ensure compliance with data privacy regulations (like GDPR) and manage data security risks effectively.
Main functions of a Data Leader:
● Hands-On Coding (Context-Dependent): At the company, the Data Leader may need to code, especially for prototypes, exploratory analysis, or setting up initial infrastructure, including ETL/ELT or machine learning models.
● Automation and Scripting: Writing scripts to automate data tasks (e.g., extraction, monitoring, reporting), especially in resource-constrained environments.
● Data Management: Oversee the collection, storage, and processing of data across the organization, ensuring the use of appropriate tools and techniques for large-scale data handling.
● Data Analytics: Extracts insights from data, using statistical methods, machine learning, and business intelligence tools.
● Strategy and Vision: Define the company’s data strategy and align it with business objectives, ensuring that data initiatives support key business outcomes.
● Data Governance: Develop and implement data governance policies to ensure data security, privacy, and compliance with industry standards.
● Performance Monitoring: Track and report on key data metrics, making sure the organization is leveraging data effectively to achieve its goals.
Technical Skills required:
Main Technical skills of a Data Leader:
● Data Architecture and Engineering: Understanding of data pipelines, data storage (data lakes, data warehouses), and architecture frameworks. Familiarity with tools like Apache Hadoop, Spark, and cloud data services (AWS, Azure or Google Cloud)
● Data Analytics and BI Tools: Expertise in data visualization, reporting, and analytics tools such as Power BI, Quicksight, or Looker, as well as experience with programming languages like SQL and Python for advanced analytics.
● Machine Learning and AI: Strong grasp of machine learning algorithms, Al tools, and the practical application of these techniques in business contexts. Familiarity with frameworks like TensorFlow or PyTorch is beneficial.
● Cloud and Big Data Technologies: Experience with cloud platforms (AWS, GCP, Azure) and big data processing tools like Apache Kafka, Flink, or Databricks for handling massive datasets.
● ETL/ELT and Data Integration: Proficiency in ETL/ELT tools (e.g., AWS Glue or similar) to manage the flow of data from different sources into centralized systems.
● Database Management: Knowledge of both relational (SQL) and non-relational (NoSQL) databases, including PostgreSQL and Cassandra or similar.
● Data Quality Management: Skills in establishing data quality frameworks, ensuring data accuracy, consistency, and timeliness across the organization.
● Statistical Analysis and Experimentation: Strong foundation in statistical methods, A/B testing, and causal inference techniques for data-driven decision-making.
● APIs and Data Products: Experience in designing and using APIs to integrate data across systems, as well as understanding how to build scalable data products.
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