Overview

We are looking for a Senior Data Engineer to design, build, and operate modern data solutions that power our data products, analytics, integrations, reporting, and emerging AI capabilities.

Responsibilities

• Design, develop, and maintain scalable data pipelines supporting operational, analytical, reporting, and AI use cases.
• Build batch, incremental, near-real-time, and event-driven data processing solutions.
• Develop data ingestion, transformation, orchestration, and delivery workflows using modern cloud data platforms.
• Design and implement data models optimized for performance, maintainability, and consumption.
• Develop reusable engineering components and patterns rather than one-off solutions.
• Optimize pipelines and data processing for reliability, scalability, performance, and cost.
• Contribute to technical design discussions and architecture decisions.
• Design and implement integrations across enterprise applications, data platforms, and third-party systems.
• Develop reusable integration services and data-access capabilities.
• Build secure REST and GraphQL APIs to expose data and platform capabilities.
• Implement event-driven and asynchronous integration patterns where appropriate.
• Integrate systems using APIs, connectors, messaging, events, and modern data-sharing techniques.
• Apply appropriate authentication, authorization, versioning, monitoring, and API lifecycle practices.
• Build automated data validation, reconciliation, and quality controls into pipelines and data products.
• Implement appropriate unit, integration, and data testing as part of development.
• Build monitoring and observability into pipelines, APIs, and integration services.
• Troubleshoot complex production issues and perform root-cause analysis.
• Implement preventive improvements rather than repeatedly addressing symptoms.
• Continuously improve the reliability, resiliency, maintainability, and performance of production solutions.
• Use AI-assisted development throughout the engineering lifecycle, including design, coding, testing, documentation, troubleshooting, and optimization.
• Apply appropriate technical review and validation to AI-generated code and engineering artifacts.
• Practice spec-driven development by defining clear requirements, data contracts, API contracts, acceptance criteria, and test expectations before implementation.
• Use AI to accelerate engineering delivery while maintaining security, maintainability, and quality.
• Contribute to the development of data solutions supporting AI agents, RAG, semantic models, machine learning, and other AI-enabled applications.
• Experiment with emerging engineering capabilities and share effective practices with the broader team.
• Implement data quality, lineage, metadata, security, and auditability requirements within data solutions.
• Build data pipelines and integrations appropriate for sensitive and regulated data.
• Follow established governance, risk, security, and compliance requirements.
• Support secure and auditable processing of KYC, UCC, public-record, and other business data.
• Maintain clear technical documentation for data flows, APIs, transformations, and operational processes.

Qualifications

• . Bachelor's degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent practical experience.
• . 6+ years of experience in data engineering, integration engineering, software engineering, or related roles.
• . Strong SQL skills with experience in data modeling, database design, and performance optimization.
• . Hands-on experience designing and developing scalable ETL/ELT pipelines and data-processing solutions.
• . Programming
• experience with Python, PySpark, Scala, Java, C#, or a comparable language.
• . Understanding of data quality, governance, security, lineage, and cloud identity concepts.
• . Practical experience using AI-assisted development tools in day-to-day engineering.
• . Strong problem-solving, communication, and collaboration skills.
• . Familiarity with concepts such as KYC, UCC, public records, GRC, or SOC 2.
• Modern Data Platforms | Databricks | Microsoft Fabric | Snowflake | Cloud Data Services | SQL | Python / PySpark | Apache Spark | Data Modeling | ETL/ELT | REST & GraphQL APIs | Event-Driven Architecture | Data Integration | Data Quality & Observability | Data Governance & Lineage | CI/CD & DevOps | AI-Assisted Engineering | AI Agents & RAG
• Our Interview Practices