AI Data Engineer
Aquent
Job Overview
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Job Description
AI Data Engineer
Are you ready to make a significant impact at the forefront of technological innovation? Aquent is partnering with a leading global technology company dedicated to shaping the future through groundbreaking advancements. This organization is renowned for its commitment to developing robust, scalable solutions that empower millions worldwide. We are seeking a highly skilled and passionate individual to join a pivotal team responsible for building and maintaining the foundational data platforms and pipelines that drive critical insights and power next-generation AI initiatives. Your expertise will directly contribute to designing, developing, and maintaining the efficient and reliable data infrastructure that underpins the company’s strategic decisions and product evolution, truly unleashing the power of data.
What You’ll Do
As a core member of our dynamic data engineering team, you will be instrumental in transforming raw data into actionable intelligence. Your contributions will directly impact the efficiency, scalability, and reliability of our client’s data ecosystem, fostering innovation and supporting data-driven growth.
- Design, build, and maintain scalable data platforms and pipelines utilizing cutting-edge tools and technologies.
- Collaborate closely with diverse stakeholders to meticulously gather business requirements and translate them into robust technical specifications.
- Develop and implement sophisticated data models that effectively support advanced analytics and comprehensive reporting needs.
- Champion data quality and governance by implementing rigorous validation, consistency checks, and reliability measures.
- Partner with cross-functional teams, including data analysts, data scientists, and business leaders, to deliver high-quality data solutions that meet evolving demands.
- Continuously monitor and optimize data pipelines for peak performance, scalability, and cost-efficiency.
- Establish and implement comprehensive monitoring and observability metrics to proactively ensure data quality and detect anomalies within data pipelines.
- Create clear, comprehensive documentation for data processes and effectively communicate complex technical concepts to both technical and non-technical audiences.
Must-Have Hard Skills
- A Bachelor’s degree in Computer Science, Engineering, Information Systems, or a closely related field.
- A minimum of 2 years of professional experience in data engineering with experience in Python, SQL, Kubernetes, Airflow and Scala.
- Demonstrated proficiency in data warehouse management, alongside strong experience in building and maintaining robust data pipelines and ETL processes.
- Excellent verbal and written communication skills, with the ability to clearly convey technical information to diverse audiences.
- Proven ability to thrive and contribute effectively within a collaborative, cross-functional team environment.
- Strong analytical capabilities, including the ability to gather complex business requirements and debug intricate issues across various data systems.
Nice-to-Have Qualifications
- Experience with leading cloud platforms such as AWS, GCP, or Azure.
- Familiarity with various industry-leading data warehousing technologies.
- Knowledge of data governance and data security best practices.
Key skills/competency
- Data Engineering
- Python
- SQL
- Kubernetes
- Airflow
- Scala
- Data Pipelines
- ETL
- Data Modeling
- Cloud Platforms
How to Get Hired at Aquent
- Research Aquent's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Tailor your resume: Highlight relevant data engineering experience, Python, SQL, Kubernetes, Airflow, and Scala skills, emphasizing achievements.
- Showcase your portfolio: Prepare examples of scalable data pipelines, ETL processes, and data modeling projects you've designed or optimized.
- Prepare for technical interviews: Practice coding in Python and SQL, understand Kubernetes and Airflow concepts, and discuss data architecture best practices.
- Demonstrate soft skills: Be ready to discuss your collaboration experience, communication skills, and analytical approach to problem-solving.
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