Artefact is a new generation of data service providers specialising in data consulting and data-driven digital marketing. It is dedicated to transforming data into business impact across the entire value chain of organisations. We are proud to say we’re enjoying skyrocketing growth.
The backbone of our consulting missions, today our Data consulting team has more than 400 consultants covering all Artefact's offers (and more): data marketing, data governance, strategy consulting, product owner…

What you will be doing?
As a Data Engineer, your role involves crafting and maintaining robust data pipelines, utilising Python and SQL, to ensure efficient extraction, transformation, and loading (ETL) of data.

Your responsibilities will include:

• Data Pipeline Development: Building and optimising data pipelines to facilitate seamless data flow across systems and platforms.
• Database Management: Managing databases, ensuring their integrity, and implementing data storage and retrieval solutions.
• Cloud Services Integration: Leveraging cloud services such as MS Azure, GCP, and AWS to architect and deploy scalable data solutions.
• Machine Learning Integration: Collaborating with teams to integrate machine learning models into data pipelines for enhanced data processing.
• Utilising Spark & Kafka: Implementing and working with Spark and Kafka for real-time data processing and analytics.

What we are looking for?

• 2-5 years: Data Engineer
• Proficiency in Python, SQL, and database management.
• Experience with Data Pipelines ETL, Cloud Services (MS Azure, GCP, AWS), ML Modeling, Spark & Kafka.
• Proven problem-solving skills and a solution-oriented mindset.
• Experience working with business stakeholders either internally or externally
• Excellent communication skills to collaborate effectively within teams and with stakeholders.
• Strong business acumen with an interest in business-facing roles.
• Adaptability and a start-up mentality to thrive in a dynamic environment.
• Minimum of a bachelor's degree in Computer Science, Electronics, and Communication Engineering degrees.

Artefact is the place to be: come and build the future of data and AI

Innovation: We have a passion for creating impacting projects and believe innovation can come from anyone.
Action: We make things rather than telling people how to make them.
Collaboration: We believe in bringing talented people together, winning together, and in learning from each other.

About us:

Artefact is a global services company that sits at the intersection of consulting, data science, AI technologies and marketing. Our 1700+ people break Business and Tech silos and transform organizations into consumer-centric leaders using digital, data and AI.

Key Responsibilities:

• Data Quality Analysis & Remediation: Collect, extract, and clean data from various sources to prepare comprehensive datasets for analysis This includes handling missing data, removing inconsistencies, and ensuring the data is structured correctly for further analysis.
• Analyze data using appropriate statistical techniques and tools to uncover trends, patterns, and anomalies in the information. This could involve anything from simple descriptive analysis to more complex exploratory data analysis
• Validate and cross-check analysis results to ensure accuracy. This involves verifying calculations, comparing findings against external or historical data for consistency, and investigating any irregularities in the results. Data Analysts are expected to be vigilant about data quality during analysis, catching any issues that might have passed initial data processing.
• Create clear, concise reports and visualizations to communicate findings. While the focus is on analysis rather than just creating dashboards, analysts will use tools like Power BI to illustrate key insights. They should present data in an understandable way for internal stakeholders, writing summaries that interpret the numbers and highlight important conclusions.
• Work closely with statisticians and other team members to interpret results and refine analysis approaches. This collaborative approach ensures that the analysis aligns with methodological standards and project goals. Analysts may adjust their methods based on feedback, contribute to discussions on what the data means, and help integrate their findings into broader census reports.

Key Qualifications:

• Bachelor’s degree in a quantitative field such as Statistics, Data Science, Economics, Mathematics, or similar. This educational background provides a solid foundation in data analysis techniques and statistical reasoning.
• Strong proficiency in data analysis tools and languages, particularly SQL for database querying, Excel for data manipulation, and programming in Python or R for more advanced analysis and automation. Experience with statistical libraries or packages (e.g., pandas, R’s tidyverse) is highly valuable.
• Demonstrated ability to perform in-depth data analysis and not just generate visuals. We need analysts who can write complex queries, perform calculations, and apply statistical tests if necessary – beyond assembling dashboard components. An analytical mindset and attention to detail are crucial for interpreting data correctly and spotting outliers or errors.
• Familiarity with business intelligence and visualization tools, especially Power BI, to aid in presenting analysis outcomes. While creating polished dashboards is not the primary focus, the analyst should be capable of using Power BI (and similar tools like Tableau if needed) to turn data into interpretable charts and graphs for reporting purposes. They should also understand data visualization best practices to communicate information effectively.
• Excellent communication skills, both written and verbal, with the ability to explain data findings in layman’s terms. A collaborative attitude is important, as Data Analysts will frequently interact with other teams (IT, field operations, subject-matter experts) to gather context and ensure that analyses meet the needs of the census project. They should be proactive in sharing insights and contributing to data-driven decisions within the organization.