Data Engineer managing scalable data ecosystems for actionable business intelligence and cross - functional stakeholder collaboration. Optimizing ETL/ELT pipelines and ensuring data integrity and security.
About the role
- Data Engineer to build and support data pipelines and models for analytics and machine learning. Working with ADI's Product Data Science team to leverage extensive data and automate processes.
Responsibilities
- Designing and implementing data pipelines to efficiently extract, transform, and load (ETL) from various data sources into a central repository or data warehouse
- Deploy and maintain scalable production pipelines for AI models, ensuring seamless integration with existing systems, continuous performance monitoring, and iterative improvements based on real-world feedback
- Working on transformations and contributing to building the dimensional model of ADI product data
- Building and maintaining scalable and robust data infrastructure, including data lakes, and distributed computing systems that are equipped to handle large volumes of data
- Implement, and build data solutions using Spark, Python, Databricks, and the AWS ecosystem (S3, Redshift, EMR, Athena, Glue).
- Implementing data validation and cleaning processes to ensure data quality and monitoring data pipelines for errors or anomalies
- Optimizing data storage and retrieval processes to enhance performance and reduce latency, particularly through techniques such as indexing, partitioning, and caching
- Implementing data security and privacy measures, including data encryption, access controls, and compliance with data governance policies and regulations
- Collaborating with data scientists and data analysts to understand metrics and data requirements and to provide them with access to the necessary data sets and data systems
- Adapt, learn, grow, and teach to deliver world-class products and platforms
- Bring a curious mind to current business problems and opportunities, expanding your understanding of all products in the ADI ecosystem
Requirements
- Strong programming skills and in Python
- Strong expertise in database and query languages like SQL, NoSQL
- Knowledge of data engineer skills such as data management, data visualization, and familiarity with data architecture
- Knowledge and hands-on experience in building scalable data platforms and reliable data pipelines using technologies such as Spark, Databricks, AWS Kinesis, and/or Kafka
- Solid understanding of MLOps principles and model versioning
- Experience working with large volumes of metadata and schemas
- Hands-on experience in ETL/ELT and data integration
- Data warehousing knowledge, including data modeling, data security, and data governance understanding
- Understanding how the role complements others working with machine learning, data science, algorithms, business intelligence
Benefits
- Health package
- Insurance in case of serious illness, surgical intervention, professional illness, and insurance from the consequences of an accident
- Flexible working hours
- English classes during working hours
- Employee referral bonus program
- Corporate social events and team buildings
- Food and drinks: Free use of coffee machines, free fruit and snacks
- Well-equipped office
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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