Data Engineer

Job Summary

Responsible for designing, developing, and implementing scalable, secure, and efficient data solutions that enable and fuel the implementation of Machine Learning (ML) and Artificial Intelligence (AI) models to drive business innovation through automation and predictive modeling.

Key  Responsibilities:

Tech Lead (Scrum Team):

• Lead the Data Science Scrum Team, ensuring effective collaboration and communication among team members.
• Participate in sprint planning, daily stand-ups, retrospectives, and backlog refinement meetings.
• Ensure technical excellence and adherence to Agile principles and best practices.

Data architecture design:

• Collaborate with data scientists, engineers and stakeholders to design and implement data architectures that meet the needs of our ML/AI projects.

Data pipeline development:

• Develop and maintain scalable, reliable, and efficient data pipelines using tools such as Apache Beam, Kafka, or similar technologies.
• Develop and maintain ETL pipelines, consumption from data warehouses or big data platforms as needed (Snowflake).

Big Data processing:

• Design and implement big data processing workflows using frameworks like Spark, or similar, to process large datasets.

Data Integration:

• Integrate data from various sources (e.g., structured, unstructured, API) into a unified data landscape.

Data quality and governance:

• Ensure high-quality, well-governed data by implementing data quality controls, data validation rules, and data lineage tracking with the Global Data Operations team.

Cloud computing:

• Leverage cloud platforms like AWS, Azure, or Google Cloud to design and deploy scalable data architectures.

ML/AI infrastructure:

• Collaborate with the Data Science team to develop and maintain infrastructure for deploying ML/AI models, including model serving, data storage, and metadata management.

DevOps:

• Implement DevOps practices to ensure efficient deployment, monitoring, and maintenance of data pipelines and architectures.

Technical leadership:

• Lead by example, contributing to the development of best practices within the team.

Staying up to date:

• Stay current with industry trends, tools, and technologies in big data, cloud computing and AI/ML.
• Develop new skills and expertise to support evolving reporting requirements.

Requirements:

Education: Bachelor's degree in Computer Science, Engineering, or related field; Master's degree preferred. Experience:

• 8+ years of experience in data engineering, with a focus on data science, preferably in the chemical industry or a related domain.
• 3+ years of experience as a Tech Lead or similar leadership role.
• Familiarity with Agile methodologies (Scrum) and collaboration tools (e.g., Jira).

Technical Skills:

• Proficiency in programming languages such as SQL, Python, Scala, R.
• Experience in delivering of Big Data project, with processing frameworks such as Apache, Spark, Hadoop, or Snowflake ecosystem.
• Hands-on experience with data engineering tools such as Apache Bream, Kafka.
• Knowledge of ETL processes, data warehousing, big data technologies (e.g., Snowflake, Kafka, Atlan…), and at least one Cloud data platform, such as AWS, Azure, or Google Cloud solutions.
• Experience with various Database management systems (relational databases, NoSQL databases, graph database)
• Experience with data governance frameworks such as Data Catalog or similar tools.
• Familiarity with containerization (Docker) and orchestration (Kubernetes).
• Knowledge of data science methodologies and ML/AI concepts.

Competencies:

• Drive for results with strong customer focus,
• Dealing with ambiguity, problem solving and creativity,
• Good work/project management skills with timely decision making,
• Fluency in English with excellent written and verbal communication skills; additional languages preferred,
• Ability to work in a fast-paced environment with changing priorities,
• Excellent communication and collaboration skills
• Strong problem-solving and analytical abilities.

Nice to Have:

Certifications: SnowPro or similar certification. Industry Knowledge: Familiarity with the Flavor industry, particularly in areas like process optimization, complex opportunity tracking. Project Experience: Experience leading or contributing to data science projects that involved complex data processing.

We are a global leader in taste, scent, and nutrition, offering our customers a broader range of natural solutions and accelerating our growth strategy. At IFF, we believe that your uniqueness unleashes our potential. We value the diverse mosaic of the ethnicity, national origin, race, age, sex, or veteran status. We strive for an inclusive workplace that allows each of our colleagues to bring their authentic self to work regardless of their religion, gender identity & expression, sexual orientation, or disability.

Visit IFF.com/careers/workplace-diversity-and-inclusion to learn more