Data Engineer

Uni Systems

Ispra, Italy

Description

In this role, you will have the opportunity to work closely with our customers in the public sector and you will be responsible for developing new business by identifying profitable opportunities, maintaining your client portfolio by building strong, long-lasting relationships, and monitoring the progress of the projects, with the aim to provide excellent client service and added value services.

What will you be bringing to the team?

Collection of business requirements and development/customisation/deployment/maintaining/improvement of software applications in the field of data mining, Natural Language Processing (NLP), Machine Learning (ML) and/or Artificial Intelligence (AI);
Interaction with the business analysts, customer, users, project leaders and developers; Interact with data stewards and other IT stakeholders to define the data rules;
Training of custom machine learning / deep learning models based on structured and unstructured data;
Selecting features, building and optimizing classifiers using machine learning techniques;
Processing, cleansing, and verifying the integrity of data used for analysis;
Design the IT architecture for solutions in the NLP / ML / AI fields, and coordinate its implementation considering master- and meta-data management concepts;
Analysing data architecture for consistency, completeness, accuracy and reasonableness; proposingand implementing related improvements;
Contributing for the analysis of data management vision, strategy and policy and derive the IT requirements;
Scripting and programming, and related testing and tuning;
Defining data controls and implement/debug/test/recommend actions to ensure data quality and integrity, and for improvement on methodology;
Creating automated anomaly detection systems and constant tracking of its performance;
Contributing to the design and implementation of the analytics architecture and its solution stack (including performance aspects, physical design, capacity dimensions etc…);
Writing the different documentation associated with the tasks and liaise with other project teams as necessary to address cross-project interdependencies

Requirements

What do you need to succeed in this position?

Master Degree

At least 2 years experience in:

Python 3 libraries for data management and AI (including, but not only limited to, NumPy, SciPy, Pandas, Keras and TensorFlow).
RDBMS (e.g. Oracle DBMS, PostgreSQL or MySQL), NoSQL databases (at least ElasticSearch, MongoDB, Apache Solr), and either Graph Databases (at least Neo4j) or TripleStore Databases (at least OpenLink Virtuoso). Data modelling and performance tuning for analytical queries.
AWS and/or Azure, Linux and Bash.
Excellent and proven knowledge in Elasticsearch and good knowledge of the other Elastic Stack components (Kibana, Beats & Logstash)
Excellent and proven knowledge of SQL tooling (RDBMS, NoSQL) and related query languages
Excellent knowledge of Data Analytics techniques and tools over big datasets, non-structured databases as well as data lakes
Knowledge of data visualisation tools (such as D3.js, GGplot, etc.) and/or of business intelligence tools (e.g. PowerBI, Tableau, SAS, SAP, GoodData…)
Knowledge of Data Management
Proven knowledge in Data Engineering tasks (including, but not limited to building systems and pipelines, optimisation of algorithms, definition of architectures and functional blocks aligned with software engineering best practices)
Proven knowledge in Natural Language Processing (NLP), Neural Networks and Deep learning libraries, and Classical Learning (e.g. Pattern search, Clustering, Classifications) algorithms
Proficient in continuous code delivery and unit testing and agile software development methodologies
Knowledge of architectural design and implementation of scalable modern data stores.
Knowledge in at least three of the following areas: predictive (forecasting, recommendation), prescriptive (simulation), sentiment analysis, topic detection, social media crawling and processing, plagiarism detection, trends/anomalies detection in datasets, recommendation systems
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Ability to write, debug and validate code and results, to learn new approaches and technologies and to scout for new solutions efficiently and fast
Ability to understand, speak and write English (B2)
Excellent and proven knowledge in Python 3, package managers (PIP, Conda) and Jupyter. Knowledge of Perl, Python, Matlab, R is an asset.