Application Developer Intermediate (Hybrid) - Deadline 25/03/26

The following tasks are required:

a) Data collection: create pipelines to interact with internal and external databases, including web APIs. Design, develop, document and maintain processes for data collection, integration, transformation and dissemination including automation processes

b) Data mining and data analysis: Design, develop, document and maintain data analytics and statistical analysis products, reports and surveys

c) Data processing: Design, develop, document, maintain and ensure data quality, data harmonization and cleaning

d) Prediction: Development of predictive models, such as machine learning, to identify relevant features and predict future events

e) Artificial Intelligence: Design, develop, document and maintain Artificial Intelligence and machine learning solutions, including Natural Language Processing and generative AI techniques, ensuring appropriate data quality, validation and integration with existing data platforms

f) Data reporting: Design, develop, document and maintain BI models, reports, dashboard, security, and automation

g) Data visualization: Design and build interactive and attractive visualizations of data

h) Collaborate with data architect/engineer to design, develop, document and maintain data architecture, data modelling and metadata

i) Facilitate analysis and integration processes on the overall data ecosystem, including data governance, by working with data providers to fill data gaps and/or to adjust source-system data structures

j) Participation in meetings with the project and data teams

Key technology areas where above tasks are to be delivered include:

• Advanced data and statistical analysis, techniques and tools using Python

• Artificial Intelligence and machine learning techniques in Python, including Natural Language Processing, generative AI, embeddings and semantic search

• Creating reports, visualizations, and dashboards using Python (e.g. Databricks, Jupyter notebooks, voila dashboards) and Power BI

• Development and data processing using Python for structured, semi-structured and unstructured data types and related file format (e.g. JSON, Parquet, Delta)

• Advanced Power BI Online Services and best governance practices

• Gathering business requirements and transforming it into data collection, integration and analysis processes

• Knowledge of modelling libraries, including OLS regression, generalized linear models and machine-learning, in Python

• Data modelling, principles and methods

Supplementary technology areas (added value, not requirements) where above tasks are to be delivered include:

• Advanced data and statistical analysis, techniques and tools using R

• Microsoft On-Prem and Azure Data Platform tools (such as Azure Data Factory, Azure Functions, Azure Logic Apps, SQL Server, ADLS, Azure Databricks, Microsoft Fabric/Power BI, Azure DevOps, Azure AI Services)

• Databricks ecosystem, Apache Spark and Python and R data processing libraries

• SQL, Power M and DAX

• Data governance and data management standards, policies, processes, metadata, quality

• Data Lakes and Data Lakehouse architecture, concepts and governance

• Master data and reference data management concepts

• Business glossaries, data dictionaries, and data catalogues

• DAMA Data Management best practices and standards

• Web APIs and OpenAPI standard

• Survey tool e.g. EU Survey

Level: Intermediate

Delivery mode : Hybrid (Malta, La Valletta and remote)

ratio of 60% offsite and 40% onsite modes