OUR PURPOSE
To catalyse an enriched future for all.
Palladium is a global leader in the design, development and delivery of Positive Impact- the intentional creation of enduring social and economic value. We work with corporations, governments, foundations, investors, communities and civil society to formulate strategies and implement solutions that generate...
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Collaborate with stakeholders to understand their data needs and develop analytical solutions to address business challenges.
Design, develop, and maintain scalable data models, pipelines, and architectures for efficient data extraction, transformation, and loading (ETL) processes.
Perform data cleansing, aggregation, and validation to ensure data accuracy, completeness, and consistency.
Analyze complex data sets using statistical techniques, data mining, and machine learning algorithms to identify patterns, trends, and insights.
Develop and implement data visualizations, dashboards, and reports to effectively communicate analytical findings to both technical and non-technical stakeholders.
Collaborate with data scientists and analysts to support their analytical needs and facilitate the integration of data models into production systems.
Monitor data quality and performance of analytics solutions, identifying and resolving issues to ensure reliable and accurate outputs.
Stay up-to-date with emerging trends, technologies, and best practices in analytics, data engineering, and data visualization.
Contribute to the continuous improvement of data processes and infrastructure by identifying opportunities for automation, optimization, and efficiency gains.
Required Qualifications:
Bachelor's Degree in Computer Science, Engineering, Statistics, or a related field.
Proven work experience as an Analytics Engineer or in a similar role, with a focus on data engineering, statistical analysis, and data visualization.
Proficient in programming languages such as Python, R, or SQL, with experience in data manipulation, analysis, and automation.
Strong understanding of database systems, data warehousing, and ETL processes.
Experience with data visualization tools such as Tableau, Power BI, Apache Superset, Metabase or similar.
Solid knowledge of statistical analysis techniques and machine learning algorithms.
Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and big data technologies (e.g., Hadoop, Spark) is a plus.
Strong problem-solving skills with the ability to analyze complex datasets and derive meaningful insights.
Excellent communication skills with the ability to effectively present analytical findings to both technical and non-technical audiences.
Detail-oriented and highly organized with the ability to manage multiple priorities and meet deadlines in a fast-paced environment.
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