Data Engineer
Selby Jennings is partnered with an established tax advisory firm on a tech team expansion project. They are looking for a skilled data engineer/software engineer, ideally from the financial industry, to develop, maintain, and improve a variety of tech functions in their Dallas office. This firm is not able to offer sponsorship, and an ideal candidate is one of which with notable career progression and a passion for the financial space.
Responsibilities:
- Design and maintain scalable, secure data pipelines using a variety of programming languages and tools within cloud environments like AWS or Azure, aligned with the Platform Reference Architecture.
- Collaborate cross-functionally with management, product teams, and clients to understand data requirements, deliver robust cloud-based solutions, and continuously improve user experiences through feedback and iteration.
- Contribute to Agile development teams by designing, testing, deploying, and supporting high-performance data solutions that drive business value and decision-making.
- Ensure quality and compliance by performing rigorous testing, peer reviews, and aligning with internal data security policies and client requirements.
- Stay ahead of technology trends by engaging in continuous learning, participating in tech communities, and mentoring peers while upholding values of diversity, transparency, and ethics.
Requirements:
- Bachelor's or Master's degree in a relevant discipline such as Computer Science, Data Engineering, or a related field, with over 3 YOE in designing and developing data-centric technologies.
- Proven track record of deploying ETL solutions in production-grade environments for 3+ years.
- Extensive experience working with cloud-based data platforms, preferably AWS or Azure, and proficiency in backend or data engineering development using Python, Scala, Java, .NET, or similar languages for 3+ years.
- Strong familiarity with both Windows and Linux operating systems in mixed environment and demonstrated ability to exceed performance goals through analytical thinking and practical experience.
- Proficiency in working with relational databases; experience with NoSQL databases is a plus.
- Exposure to distributed data platforms such as Hadoop, Spark, or similar technologies is advantageous.
- Familiarity with AI/ML data pipelines is preferred.
- Experience in deploying, monitoring, and maintaining scalable data pipelines in live production settings.
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