Senior Python Data Engineer

We are

At Synechron, we believe in the power of digital to transform businesses for the better. Our global consulting firm combines creativity and innovative technology to deliver industry-leading digital solutions. Synechron’s progressive technologies and optimization strategies span end-to-end Artificial Intelligence, Consulting, Digital, Cloud & DevOps, Data, and Software Engineering, servicing an array of noteworthy financial services and technology firms. Through research and development initiatives in our FinLabs we develop solutions for modernization, from Artificial Intelligence and Blockchain to Data Science models, Digital Underwriting, mobile-first applications and more. Over the last 20+ years, our company has been honored with multiple employer awards, recognizing our commitment to our talented teams. With top clients to boast about, Synechron has a global workforce of 14,500+, and has 58 offices in 21 countries within key global markets.

Our challenge

We are seeking a Senior Python Data Engineer with deep expertise in building scalable, production-grade data pipelines and models, particularly within the financial domain. The ideal candidate will have advanced proficiency in modern Python frameworks like Fast API and Pydantic, strong data manipulation skills, and extensive experience with relational and NoSQL databases. Knowledge of graph databases, LLMs, and MLOps practices is highly desirable. Join us to innovate at the intersection of data engineering, AI, and financial risk management, delivering impactful, high-performance solutions.

Additional Information*

The base salary for this position will vary based on geography and other factors. In accordance with law, the base salary for this role if filled within Irving, TX is $120k - $130k/year & benefits (see below).

The Role

Responsibilities:

• Design, develop, and deploy robust, scalable data pipelines to process large volumes of structured and unstructured financial data.
• Implement AI-driven solutions, leveraging LLMs and Generative AI to enhance data quality, enrichment, and analysis.
• Engineer and productionize predictive and prescriptive models in collaboration with quantitative and business teams to deliver measurable value.
• Analyze complex financial datasets, with a focus on credit risk, to uncover patterns, insights, and innovative solutions.
• Serve as a technical partner to stakeholders, translating business requirements into high-performance, resilient data systems.
• Stay abreast of latest advancements in data engineering, AI, and ML to foster continuous innovation and improvement within the team.

Requirements:

• Expert-Level Python: Deep, hands-on proficiency with modern Python (3.11+).
• Modern Frameworks: Proven experience building high-performance, production-ready services and data models using the latest Python frameworks, including FastAPI and Pydantic.
• Data Tooling: Strong command of core data manipulation and analysis libraries (e.g., Pandas, NumPy, Polars).
• Database Proficiency: Advanced SQL skills and extensive experience working with large-scale relational databases (e.g., Sybase IQ, PostgreSQL, Oracle).
• Educational Foundation: Bachelor’s degree in computer science, Engineering, or a related quantitative field (or equivalent practical experience).
• Problem-Solving Mindset: A proven ability to dissect complex, often ambiguous problems and engineer elegant, effective solutions.

Preferred, but not required:

• Graph Technology: Practical experience with graph databases, specifically Neo4j Enterprise, and graph data modeling concepts.
• Diverse Database Experience: Proficiency with various database systems, including relational databases like PostgreSQL and NoSQL databases like MongoDB.
• GenAI & LLM Experience: Hands-on experience with modern AI frameworks like Lang Chain, Llama Index, or Hugging Face Transformers.
• Big Data Expertise: Familiarity with distributed computing frameworks like Apache Spark (PySpark) or Dask.
• Financial Domain Knowledge: Prior experience in the financial services industry, especially within risk management, is a significant plus.
• MLOps: Understanding of MLOps principles and tools for model versio