MIT Analyst Engineer

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role, MIT Analyst Engineer, are to:

• Develop strategies to identify, acquire and use appropriate data sets to develop practical solutions and support decision making;
• Define the strategy and engineering guidelines for major data platforms jointly with the data architect;
• Ensure to implement efficient data access control mechanisms to different raw data sources both on-premises and cloud environments;
• Identify, design, and implement internal process improvements, automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability;
• Create and maintain optimal data pipeline architecture;
• Build an infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using various state of the art and big data technologies;
• Build and manage a robust, scalable, data pipeline in a hybrid-cloud environment that provides data processing capabilities to meet functional / non-functional business requirements (Architects);
• Be responsible for establishing and continuously improving sustainable, efficient, scalable, adaptable data processes and data processing applications;
• Support the development of continuous integration processes and infrastructure;
• Analyze large and complex data sets and models to gain insights and build suitable data models;
• Assemble large, complex data sets (e.g., text, time series, imagery) into a useful format for analysis using fit-for-purpose database technologies;
• Build services and tools to make data more accessible to all data consumers (tools such as searchable metadata, API gateway);
• Ensure end-to-end pipeline orchestration;
• Support the further development of existing enterprise data analytics solutions and be available as a contact person for data support;
• Be responsible for participating in the data warehouse by continuously improving ELT and ETL processes for the data lakes and data warehouses;
• Support software architecture decisions by writing, maintaining, and reviewing software code and ensures the code quality by automated testing;
• Build semantic data layers and knowledge graphs using fit-for-purpose methodology and technologies e.g., linked data concept, RDF, triple stores, graph databases to make core data structures that have already been discovered findable, accessible and reusable in an efficient way to for data knowledge workers;
• Work with the platform development team to design and use tools for data logging and repeatable data tasks to accelerate and automate data scientist duties;
• Be responsible for processing, cleaning, structuring, and improving data model and building processes to support company-wide analytics.

WHO YOU ARE

Your success will be driven by your demonstration of our LIFE values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Bachelor’s degree in computer science, management information systems, or a related discipline;
• Experience in data architecture development, data asset management, data modelling, and linked data concepts as well as data taxonomy creation;
• Experience with distributed databases (e.g., Cassandra) and NoSQL
• databases like HBase, MongoDB, Snowflake or Object stores like Blob or S3 and GraphDBs etc.;
• Experience with object oriented/object function scripting languages
• (e.g., Python, Java, C++, Scala, etc.);
• Profound experience in information architecture, data science, business intelligence or equivalent areas;
• Experience in user experience modelling, information design, and concept generation;
• Strong capability to manage implementation and maintenance of data architecture concepts and related platforms and data pipelines;
• Able to draw insights from structured and unstructured data;
• Relational databases, knows SQL dialects like e.g., T-SQL and PSQL;
• Big data technologies (Hadoop, Hive, HBase, Spark, etc.);
• Competent in data systems design and data visualization techniques;
• Ability to perform business domain analysis and business process modelling;
• Knowledge of data pipeline and workflow management tools (e.g., Airflow, DataFactory, Azkaban, Luigi, etc.);
• Knowledge of cloud computing (e.g., AWS, Azure);
• Knowledge of version control systems (e.g., GitHub, GitLab, etc.);
• Basic knowledge of network security, enterprise security, cloud security, database security;
• Knowledge of usability design and data warehousing techniques;
• Understanding of data quality standards and data cleaning processes;
• Verbal and written communication skills;
• Ability to challenge and convince the various stakeholders involved in any project.