Senior Data Engineer
hace 4 semanas
The Opportunity
We are lookingfor a Senior Data Engineer who will be at the forefront of architecting, building, and optimizing our current and future data platforms that powers our ambitious goals. Collaborating closely with cross-functional teams, including data analysts, software engineers, architects, product managers, and business stakeholders, you will design and implement robust and scalable data solutions that empower us to derive actionable insights from both structured and unstructured data sources. This role presents a unique opportunity to drive innovation by architecting data solutions that underpin our clients' success and the growth of the company.
Responsibilities:
- Take the lead on the design, development, and maintenance of our Modern Data Platform, to enable efficient data processing, storage, and analysis.
- Architect data ingestion strategies for a wide range of data types, including structured and unstructured data, ensuring real-time streaming and batch processing capabilities.
- Evaluate and implement cutting-edge technologies, tools, and frameworks to optimize data processing, storage, and streaming, while adhering to best practices.
- Champion data privacy by ensuring compliance with data protection regulations, including the processing and storage of personal data (PII).
- Define and implement effective ETL and ELT pipelines, supporting both Machine Learning (ML) initiatives and Analytics/BI needs.
- Collaborate with geographically dispersed internal teams to understand data requirements, ensuring data quality, integrity, and security throughout the data lifecycle.
- Provide technical mentorship and guidance to junior team members, promoting best practices in data engineering and architecture.
- Stay abreast of emerging trends and advancements in the field of modern data engineering and contribute to continuous improvement initiatives.
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, Mathematics, Applied Statistics, or a related field.
- 5 years+ of hands-on experience in modern data engineering, with a proven track record of architecting and implementing complex data solutions.
- Strong proficiency in relevant programming and query languages (e.g., Python, SQL, R, JavaScript)
- Strong understanding of data modeling, database design, data lake and data warehousing, and performance optimization techniques.
- Hands-on experience with at least one cloud platform (AWS, Azure, or GCP) and associated data & analytics services (e.g., Redshift, Snowflake, Synapse, Databricks, or BigQuery)
- Expertise in real-time streaming and batch processing, and the ability to choose the right tools for specific use cases (e.g., Spark, Kafka, Storm, Kinesis, Hive, Pig)
- Familiarity with ETL and ELT concepts and their application in data pipelines, and tools like dbt and Airflow.
- Proven experience architecting data solutions to support both Machine Learning (ML) initiatives and Analytics/BI requirements.
- Previous experience with data privacy and compliance, including handling personal data (PII) and adhering to relevant regulations (e.g., GDPR and CCPA).
- Excellent problem-solving skills and the ability to excel in a fast-paced, collaborative environment.
- Effective communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
- Involvement in open-source data engineering projects or active participation in data-related communities (Nice-to-have, but not required)
- Understanding of advanced analytics and Machine Learning techniques (Nice-to-have, but not required)
- Recent experience with data processing into hosted Large Language Models (LLMs) (Nice-to-have, but not required)
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