Engineer Jobs

About The Position

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• At least 3 years of hands-on data engineering or software engineering experience, including developing, deploying, and maintaining production data pipelines.
• Experience independently building and operating production pipelines on a modern lakehouse platform, including ownership of data processing, transformation, and operational reliability.
• Experience implementing data quality, lineage, or data contracts in production pipelines, including automated testing, reconciliation, validation, or traceability practices.
• Strong proficiency in Python and SQL, including developing, optimizing, testing, and maintaining production-quality data engineering solutions.
• Strong understanding of data modeling principles and the ability to design scalable, maintainable data structures for evolving business requirements and source systems.
• Experience designing and developing batch and incremental pipelines for structured and unstructured enterprise data.
• Strong software engineering fundamentals, including version control, automated testing, code reviews, debugging, and reliable, repeatable data processing workflows.
• Ability to validate technical solutions through measurable outcomes such as automated tests, benchmarks, experiments, performance metrics, or comparative evaluations.
• Understanding of data provenance, traceability, and governance requirements in complex enterprise or regulated environments.
• Strong analytical thinking, technical judgment, and problem-solving skills, including independently evaluating trade-offs and troubleshooting complex issues.
• Excellent written and verbal communication skills, including documenting technical solutions and explaining complex concepts to technical and nontechnical stakeholders.
• Ability to work with business subject matter experts, engineering teams, and globally distributed stakeholders to translate requirements into reliable data solutions.
• Ability to manage competing priorities, adapt to evolving requirements, and deliver high-quality solutions in a fast-paced, collaborative environment.
• Experience using AI-assisted and agentic development tools to improve engineering productivity, or a strong willingness to incorporate them into daily development practices.
• No professional certifications or licenses are required.

Nice To Haves

• Advanced degree.
• Experience with modern lakehouse technologies such as Databricks, Apache Spark, Delta Lake, or medallion architecture.
• Experience with cloud-based data platforms, preferably Microsoft Azure, and associated data storage, processing, and integration services.
• Familiarity with enterprise data governance capabilities such as data catalogs, classification, access controls, and lineage, including Unity Catalog or comparable solutions.
• Experience with knowledge graphs, graph databases, graph data modeling, vector search, hybrid search, or retrieval-based applications.
• Familiarity with unstructured data processing, document extraction, metadata management, content chunking, or search indexing.
• Experience with entity resolution, master data management, change data capture, or incremental data processing.

Responsibilities

• Develop, implement, and maintain scalable batch and incremental data pipelines that transform structured and unstructured information into clean, validated, business-ready datasets.
• Build and optimize data processing and transformation layers using lakehouse principles, including medallion architecture, data modeling, and integration of enterprise and legacy data sources.
• Implement automated validation, reconciliation, lineage tracking, and metadata management to ensure data accuracy, consistency, traceability, and reliability.
• Develop scalable data models, data contracts, and entity-resolution processes to establish consistent client, engagement, and document identities across enterprise data sources.
• Support data cataloging, classification, permissions, and access management for sensitive information and AI agents.
• Build and maintain knowledge graph structures, data relationships, search indexes, and retrieval pipelines for AI applications.
• Develop pipelines that capture AI agent activity, practitioner feedback, reviews, and corrections to support ground-truth datasets, AI evaluation, and improvements in data and retrieval quality.
• Establish quality standards, testing frameworks, benchmarks, and validation processes for data extraction, retrieval effectiveness, and grounding accuracy.
• Implement incremental processing, change data capture, versioning, and monitoring to keep data products, knowledge graphs, and search indexes accurate and current.
• Deploy, monitor, troubleshoot, and maintain production data pipelines using automated testing, code reviews, and operational documentation.
• Collaborate with external engineering partners to transition data solutions into internally owned, documented, maintainable production environments, including reusable code, tests, and runbooks.
• Partner with AI Engineering, Platform & Production Engineering, enterprise data teams, subject matter experts, and engineering teams in India to align solutions with enterprise standards and business objectives.
• Participate in special projects and perform other duties as assigned to advance the firm's AI and data platform capabilities.

Benefits

• Comprehensive compensation and benefits package for eligible employees.