Data Platform Professional

What are your contributions to the team?

Bombardier is looking for an experienced and enthusiastic Data Platform Professional to be a key part of our evolving Customer Services Digital Products team. As a member of the team working on top priority projects, you will support the team in the introduction, implementation and sustainability of core Digital Products for Bombardier customers. If you’re passionate about building scalable, enterprise-level systems, building next generation tools and solutions, as well as transforming huge data into insights that will aid making informed business decisions, we would be glad to have you on-board our growing team.

•    Planning and executing secure, best practice data strategies and approaches.
•    Collaborating with stakeholders (business analysts, data scientists, marketing, engineering, developers, architects) to develop and improve the current data architecture, data quality, monitoring and data availability schema.
•    Designing and building robust data ingestion & transformation pipelines and solutions needed to acquire, ingest, and process data from multiple sources and systems into modern data platforms.
•    Restructuring and wrangling data into forms suitable and valuable for a variety of downstream usage including business analytics, machine/ deep learning model development, as well as in systems and applications for operational, business and commercial purposes.
•    Creating and maintaining underlying cloud data infrastructure responsible for managing data flow from ingestion to storage, and to consumption.
•    Working cross-functionally with our consultants and tech leads to ensure solutions developed aligns comfortably within the organizational preferences on basis of technology and methodology.
•    Keeping up to date with advancements in data technologies and leveraging the initiatives to improve and scale existing data architectures leading to improved Bombardier Aviation customers’ experience.

How to thrive in this role?

Basic Qualifications and Required Skills:
•    Bachelor's/ Master’s degree in Computer Science, Engineering, Mathematics, or a related technical discipline.
•    2+ years of industry experience in data engineering, software development, business intelligence, data science, or related field with a track record of manipulating, processing, and extracting value from large datasets.
•    2+ years of work experience with ETL/ELT pipelines, data modeling, and data architecture.
•    2+ years of industry experience using Python, SQL, PySpark or similar for data manipulation.
•    1+ years experience with cloud infrastructure resources such as S3, RDS, Lambda Functions, Batch Jobs, State Machines etc.
•    2+ years experience data management and modeling fundamentals and data principles.
•    1+ year experience with distributed systems as it pertains to data storage and computing.
•    Strong analytic skills related to working with structured/unstructured datasets.
•    Effective interpersonal, communication and leadership skills.

To stand out (Preferred Qualifications)
•    Proficiency in one or more cloud-based relational and non-relational databases including PostgreSQL RDS Databases, Snowflake, Databricks etc.
•    2+ years of experience working in data engineering or architecture role in a company with large, complex data sources.
•    2+ years experience on building/operating highly available, distributed systems of data extraction, ingestion, and processing of large datasets.
•    2+ years experience working with AWS data technologies (EMR, Batch, S3, Glue, Kinesis, Lambda).
•    3+ years experience with ETL/ELT, data modeling, data injestion, transformation and processing.
•    Experienced using and developing APIs for data extraction and loading.
•    Experience in deploying machine/deep learning models (Decision Trees, Random Forest, Neural Networks, etc.) into production systems.
•    1+ years experience with one or more BI/ visualization tools such as Power BI, AWS Quicksight.
•    Understanding of GDPR and other regulatory requirements governing data lifecycle management.
•    Familiarity with software engineering best practices across the development lifecycle, including agile methodologies, coding standards, code reviews, source management, build processes, testing, and operations.
•    Data engineering certification (e.g., Azure Data Engineer Associate or AWS Certified Big Data – Specialty) or any other relevant certification is a plus.