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Data Engineer
Company Overview
This specialized AI and machine learning division of a global management consulting firm helps organizations across various industries leverage the power of data and AI to drive strategic decisions, improve performance, and foster innovation.
Job Description
As a Data Engineer, the individual will:
Contribute to cross-functional problem-solving sessions with their team and clients, from data owners and users to C-level executives, to address their needs and build impactful analytics solutions.
Have the opportunity to contribute to R&D projects and internal asset development.
Design and build GenAI applications (RAG, Agentic AI, etc) collaboratively with data scientists.
Map data fields to hypotheses and curate, wrangle, and prepare data for use in advanced analytics models.
Apply knowledge about clients’ data landscape and assess data quality.
Create and manage data environments and ensure information security standards are maintained at all times.
Design and build data pipelines for machine learning that are robust, modular, scalable, deployable, reproducible, and versioned.
Help to build and maintain the technical platform for advanced analytics engagements, spanning data science and data engineering work.
The individual will be part of a global Data Engineering community and will work in cross-functional Agile project teams alongside Data Scientists, Machine Learning Engineers, other Data Engineers, Project Managers, and industry experts. They will work hand-in-hand with clients, from data owners, users, and fellow engineers to C-level executives.
The ideal candidate is a highly collaborative individual who wants to solve problems that drive business value. They have a strong sense of ownership and enjoy hands-on technical work, and their values resonate with the team’s.
Qualifications and Skills
Degree in computer science, engineering, mathematics, or equivalent experience.
2+ years of relevant professional experience.
Ability to write clean, maintainable, scalable, and robust code in an object-oriented language, e.g., Python, Scala, Java, in a professional setting.
Proven experience building data pipelines in production for advanced analytics use cases.
Experience working across structured, semi-structured, and unstructured data.
Exposure to software engineering concepts and best practices, including DevOps, DataOps, and MLOps, would be considered a plus.
Familiarity with distributed computing frameworks, cloud platforms, containerization, and analytics libraries (e.g., pandas, numpy, matplotlib).
Experienced on Big Data platforms and tools like AWS, Azure, GCP, and tools like Spark, Kafka, Snowflake, GCS Data Proc, Azure DataFactory, AWS Glue, Apache Beam/Flink, Python/PySpark, etc.
Commercial client-facing or senior stakeholder management experience would be beneficial.
Disclaimer
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No Client Relationship: This company is not a client of SpringCube unless stated.
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