Data Engineer

• Serve as AG's primary technical counterpart during vendor-led delivery engagements: review architecture decisions, validate implementation against AG requirements, and own knowledge transfer to ensure internal capability is built
• Build and optimize cloud-based data pipelines, data lakes, data warehouses, and ETL frameworks using cloud-native tooling across major platforms (Azure, AWS, or equivalent)
• Ensure system integrations are scalable, secure, and aligned with compliance standards
• Partner with Team Innovation and functional department leaders to establish data standards, governance, and documentation
• Implement version control, CI/CD, and data workflow automation practices
• Design, build, and automate data models, ingestion pipelines, and transformation workflows across source systems and Medallion layers
• Implement monitoring processes for data quality, validation, and integrity across all platforms
• Design and build external client portal data layer: implement Row-Level Security (RLS) in Power BI, manage client-scoped semantic models, and support embedded analytics delivery to external stakeholders
• Monitor and manage Azure cloud resource costs: right-size compute clusters, implement resource tagging, configure budget alerts, and report on consumption trends
• Troubleshoot and optimize performance across APIs, integrations, and data warehouse environments

Senior Data Architect, Team Innovation

• Lead and grow a team of data engineers, providing mentorship and leadership within the function
• Build the future of the data engineering practice to deliver value to internal and external stakeholders while leveraging cutting edge technologies to dramatically accelerate quality, scale, and depth of delivery
• Design and implement scalable data pipelines to collect, process, and store large volumes of critical data from various sources
• Drive the evolution of the Business Data platform by leveraging modern data technologies like Spark, Airflow, and Databricks
• Collaborate across organizational boundaries with Product, Marketing, and Sales to convert business needs into technical requirements
• Authoritatively translate business use cases into well-thought-out data models that evolve with the business

Director, Data Engineering – Business Data

• Design, build, and debug the highest-impact pipelines, connecting our business systems, including CRM, billing, and internal platforms to our Snowflake warehouse, in both directions
• Own the data strategy and architecture across a multi-year horizon, while maintaining deep, hands-on familiarity with the systems and their edge cases
• Establish technical direction and standards for pipeline design, data modeling, and data quality across the org; personally build and ship the highest-risk or most ambiguous work
• Build, structure, and lead a growing Data organization of 5+ across Data Engineering, ML Engineering, and Analytics
• Own headcount planning, hiring strategy, and budget for the Data organization
• Partner with the Chief Product and Technology Officer and cross-functional leaders to translate company strategy into data contracts and platform investments
• Lead audits of existing integrations and automations, and determine what should be replaced and how that work should be prioritized
• Champion AI and automation enablement company-wide, ensuring agents and LLM use cases are built on a trusted data foundation, while remaining hands-on in building select enablement capabilities

Director, Data Engineering

•The Data Architect will help design, build, and evolve our next‑generation enterprise data platform & data integration pipeline.
•This role is central to our transformation toward a modern, governed, cloud‑based data ecosystem that supports all functions at Coverys (notably underwriting, claims, actuarial, finance, and enterprise analytics etc.).
•The Data Architect will help define the architectural blueprint, establish data modeling standards, and guide the development of scalable, automated data pipelines in Snowflake.
•In addition to design of the architecture, this position will work closely with business and technology teams to ensure data is accurate, trusted, and available for advanced analytics, reporting, and operational decision‑making.
•Design and maintain enterprise data architecture aligned to business domains (Policy, Party, Claims, Billing, Underwriting, etc.).
•Ensure data architecture design aligns to enterprise architecture standards & partners closely with Enterprise Architecture.
•Develop canonical and semantic data models to support analytics, reporting, and operational use cases.
•Define standards for data modeling, data quality, naming conventions, metadata, lineage, and documentation.
•Partner with business stakeholders to translate requirements into scalable data structures.
•Build ELT/ETL pipelines using modern cloud-native tools and frameworks.
•Lead the migration from legacy batch processes to automated, event-driven, or CDC-based ingestion patterns.
•Implement data quality rules, validation frameworks, and reconciliation logic.
•Optimize Snowflake workloads for performance, cost, and reliability.
•Design and oversee medallion-style data layers (bronze/silver/gold) for ingestion, curation, and consumption.
•Work with Data Governance to establish data dictionaries, lineage, classification, data quality and stewardship models.
•Ensure consistent use of canonical identifiers across systems and domains.
•Promote data-as-a-product principles and guide teams toward reusable, scalable data assets.
•Partner with business analysts, data scientists, actuaries, and analytics teams to support data needs.
•Mentor SQL developers and analytics engineers transitioning into modern data engineering roles.
•Mentor and coach the data engineering team & lead the development of pipeline and architecture platforms.
•Provide architectural oversight for major data initiatives across the enterprise.
•Support evolving business needs, as applicable.

Data Architect

• Leads the architecture, engineering, and ongoing operation of the security data lake and associated data products supporting multiple security technologies and data sources
• Establishes the technical roadmap, engineering standards, reusable design patterns, and platform operating model
• Acts as a subject matter expert among peers, with manager and senior management on all matters pertaining to data platforms and the supporting architecture
• Develop and maintain the end-to-end architecture of the security data lake, including ingestion, storage, transformation, enrichment, semantic, and consumption layers
• Ensure data pipelines comply with security policies and regulatory requirements
• Develop testing plans to ensure quality of implementation
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues
• Maintain relationship with key vendors
• Create presentations and seek IT management approval and acceptance of significant replacements or reconfigurations of the data pipeline

Senior Data Engineer

• Own data architecture end-to-end. Define how we capture, model, and serve critical business data then implement it in production. You’ll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency.
• Build mission-critical pipelines. Develop and operate streaming and batch data workflows that process high-volume events across multiple domains user activity, transactions, experimentation, marketing performance, and operational telemetry with tight guarantees for latency, completeness, and accuracy.
• Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and real-time applications. Establish and enforce modeling standards, ownership boundaries, and data contracts across teams.
• Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
• Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
• Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, low-latency data through semantic layers, APIs, and real-time query systems.

Data Engineer, Payments

• Collaborate with Product Managers, Business Analysts, DevOps teams, Engineering teams, PMO.
• Deliver scalable enterprise cloud solutions.
• Provide architectural options and technical recommendations.
• Conduct cost-benefit analysis and risk assessments.
• Lead the modernization of data platforms, ETL pipelines, and analytics ecosystems.

Lead GCP Data Architect

• Own and improve the Snowflake platform end-to-end, including the ongoing evolution of the environment as usage scales.
• Own source pipeline reliability and expansion, evaluating existing connections for reliability, freshness SLAs, edge case handling, and schema drift resilience, while building net-new integrations.
• Ensure data accuracy, quality, and compliance through governance, observability, and monitoring best practices.
• Own pipeline incident response with speed and clear communication, identifying root causes that prevent recurrence rather than just restoring service.
• Improve data architecture and storage solutions to enhance performance, reduce costs, and optimize batch workflows as usage and data volume scale.
• Operate as the technical authority on data infrastructure decisions, setting standards and guiding architectural choices that hold up as the team and the platform scale.

Staff Data Engineer

• Integrate data sets from various source systems into unified formats
• Perform ELT (Extract, Load, Transform) operations
• Cleanse and transform data by applying business rules
• Conduct data cleansing and validation
• Monitor and troubleshoot data issues
• Create and maintain conceptual, logical, and physical data models
• Build and maintain data pipelines
• Load processed data into analytical data stores
• Monitor the performance and quality of data transfers
• Integrate test suites into CI/CD pipelines
• Optimize data flows for efficiency, scalability, and reliability
• Collaborate with team members to gather requirements

• Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment.
• Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications.
• Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
• Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
• Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies.
• Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection.
• Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics.
• Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment.
• Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
• Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
• Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
• Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

Data Scientist – Signal Processing Engineer, Acoustics

• Design, implement, and oversee the enterprise data governance strategy
• Work in close partnership with the Ontology/Semantic Librarian and Data Engineering teams
• Lead the development and enforcement of data governance policies, standards, and best practices
• Collaborate with Data Engineering teams to implement governance in federated querying, GraphQL interfaces, vector databases, and RAG pipelines
• Define and maintain enterprise data stewardship processes
• Establish governance frameworks for data security, privacy, and compliance
• Design and implement data quality, metadata, and lineage strategies
• Contribute to the internal Data Marketplace by defining governance requirements for semantic data products
• Drive data governance training, change management, and adoption across business, engineering, and AI teams
• Monitor and report on governance metrics, risks, and compliance posture for the Data Unification program

Lead Data Engineer – Governance

• Lead the design, development, and governance of enterprise ontologies, semantic layers, and knowledge graphs.
• Combine deep semantic modeling expertise with hands-on implementation of graph technologies, vector databases, and federated data architectures.
• Design and build enterprise ontologies and semantic models that align business objectives with technical implementation.
• Lead the creation and maintenance of semantic layer graphs, knowledge graphs, and entity graphs.
• Develop and implement strategies for using vector databases and graph databases to enable powerful LLM-augmented search and reasoning.
• Partner with data engineering, AI, and business teams to translate business goals into ontological models.
• Contribute to the development of a unified data access layer that supports querying across diverse data sources.
• Implement and enforce enterprise security models through the ontology and semantic layer.
• Collaborate on AI integration initiatives, including building ontology-driven agents.
• Establish ontology governance processes and contribute to the internal Data Marketplace.

Lead Data and Ontology Engineer

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

• You will own and evolve our data pipelines on GCP — building new ones, hardening existing ones, improving data quality, and making clean, trustworthy data available across the organisation.
• Designing ELT/ETL processes on BigQuery and ClickHouse, building real-time pipelines on Pub/Sub and Kafka with Dataflow (and where it fits, Flink/Spark).
• Orchestrating workflows with Airflow, and ensuring data is properly cleaned, modelled, and served for analytics, ML training, and online inference.
• Partnering with ML engineers on feature pipelines, monitoring data drift, and keeping models well-fed and retrained as needed.
• Consuming and building REST APIs, integrate with third-party SaaS sources, and treating infrastructure as code.
• Collaborating closely with Infrastructure, Software Engineering, Product, and ML/AI engineers.
• Helping drive the migration off Snowflake onto GCP-native stack — and retire shadow pipelines along the way.
• Owning the orchestration layer in Airflow, including SLAs, retries, and data quality gates.
• Modeling data for analytics and for ML — including feature pipelines that serve both training and low-latency online inference.
• Capturing requirements from stakeholders and translating them into pragmatic, well-scoped data products.
• Continuously improving data quality, reliability, observability, and cost efficiency.
• Identifying new data sources worth acquiring and integrating them cleanly.

• Size, architect, and design data protection, disaster recovery, and scale-out storage solutions
• Bring new products and processes to life to meet customer demand, improve service SLAs, and minimize gaps
• Serve as the escalation point for L1, L2, and L3 support engineers
• Work directly with customers to solve challenging design and support issues
• Automate the collection of backup system health and streamline tasks
• Monitor backup systems and performance, identifying and troubleshooting issues
• Configure access using a least-privileged approach, audit, and ensure adherence to policies
• Communicate upgrade and rollback plans, and execute upgrades
• Build lasting trust with clients

Data Protection Engineer

• Develops, documents, tests, debugs, and maintains data; provides expertise and familiarity in software tools, frameworks and best practices in secure software engineering.
• Collaborates effectively with other members of the team to deliver on the team’s backlog.
• Participates in the discussion and implementation around complex capabilities.
• Develops complex features and capabilities to enhance the Early Warning core product offerings.
• Manages source code using Git.
• Receives, reviews and approves pull requests ensuring that code quality remains high.
• Processes both real time stream data and file based data through ETL frameworks and persist the data into the data store.
• Builds aggregations on the data to enable provisioning of data to the AI/ML models as input and also deliver them to the customers.
• Document complex details and decisions in Confluence.
• Builds automated tests alongside sprint work that is integrated into the CI/CD pipeline, considering environment variables, and provides recommendations.
• Able to troubleshoot complex bugs that may arise during development.
• Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data.

Senior Software Engineer – Data Engineering

• Architect, build, and deploy ML/GenAI products on cloud infrastructure (AWS or similar)
• Design and implement end-to-end AI workflows: data ingestion, feature engineering, modeling, evaluation, and deployment
• Create automated pipelines for continuous learning, model promotion, and performance monitoring
• Lead the design of ML orchestration frameworks (Airflow, Kedro, ZenML, Flyte) to ensure reproducibility and scalability
• Oversee deployment of large-scale and multi-agent AI systems with high reliability and fault tolerance
• Continuously optimize workflows for efficiency, robustness, and performance in production
• Translate complex business problems into AI solutions, including data collection, experiment design, and roadmap planning
• Develop interpretable, modular, and scalable ML systems that deliver measurable business value
• Work directly with customers and stakeholders to ensure deployed systems achieve their intended impact
• Stay current with advancements in AI/ML, including LLMs, diffusion models, graph AI, and agent architectures
• Propose and prototype new approaches for integrating emerging technologies into production products
• Develop methods to quantify and communicate AI performance and business ROI
• Promote responsible, ethical, and impactful AI practices across the organization.

AI/ML Engineer, Data Scientist

• Design, build, test, deploy, and support high-quality data products and platform capabilities for enterprise use cases.
• Deliver scalable and reliable software solutions across batch, near-real-time, and event-driven data patterns, as needed.
• Apply strong software engineering discipline, including code quality, automated testing, CI/CD, observability, and documentation.
• Drive engineering excellence across the delivery lifecycle, with a focus on stability, maintainability, performance, and reuse.
• Ensure data solutions are production-ready and aligned to enterprise standards for security, resiliency, and operational support.
• Build modern data products that are consumable, well-governed, and aligned to business and platform objectives.
• Contribute to the design and evolution of a large-scale, multi-business data platform supporting diverse data domains and consumption models.
• Define and implement scalable patterns for ingestion, transformation, storage, metadata, lineage, access management, and delivery.
• Develop reusable services, frameworks, and engineering patterns that accelerate delivery across teams.
• Support cloud and hybrid data architectures using modern storage and compute approaches.
• Architect enterprise-scale data capabilities leveraging Snowflake, Apache Iceberg, Oracle Exadata, Azure, and other relevant technologies.
• Evaluate current-state architecture, identify opportunities for simplification and modernization, and recommend target-state solutions.
• Make sound design decisions that balance business outcomes, risk management, scalability, cost, and speed of execution.
• Contribute to enterprise engineering standards, architecture patterns, and platform guardrails.
• Lead through technical depth, strong judgment, and the ability to convert strategy into practical engineering outcomes.
• Translate business priorities and platform strategy into actionable technical roadmaps and delivery plans.
• Partner with product, architecture, infrastructure, security, governance, and business stakeholders to align on priorities and sequencing.
• Influence decisions across the broader organization through strong communication, partnership, and credibility.
• Identify and address technical debt, platform gaps, and delivery risks in a proactive and structured way.
• Contribute to longer-term platform strategy while ensuring strong near-term execution against commitments.
• Work effectively across a matrixed organization and multiple lines of business.
• Build strong relationships with engineering teams, platform teams, architects, and business partners.
• Communicate complex technical topics clearly to both technical and non-technical stakeholders.
• Mentor team members and help elevate engineering and delivery practices across the organization.
• Demonstrate ownership, accountability, curiosity, and a strong bias for execution.

Senior Director, Data Architect

• Design, build, and maintain scalable ingestion pipelines for market, reference, tick, and alternative data from a diverse set of external vendors.
• Own the normalization, validation, storage, and lifecycle management of research datasets, ensuring data is accurate, consistent, and readily accessible for quantitative research and simulation.
• Develop and optimize Python- and SQL-based data processing workflows supporting multiple asset classes, including equities, options, futures, fixed income, ETFs, and FX.
• Partner with quantitative researchers, data architects, and infrastructure teams to onboard new datasets, improve data quality, and deliver reliable research-ready data.
• Build monitoring, automation, and operational tooling to ensure the reliability, performance, and scalability of the firm's data platform.
• Document data pipelines and engineering best practices while contributing to the ongoing evolution of Trexquant's research data infrastructure.

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Data Engineer jobs in New York

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Senior Data Architect, Team Innovation

Director, Data Engineering – Business Data

Director, Data Engineering

Data Architect

Senior Data Engineer

Data Engineer, Payments

Lead GCP Data Architect

Staff Data Engineer

Data Engineer

Data Scientist – Signal Processing Engineer, Acoustics

Lead Data Engineer – Governance

Lead Data and Ontology Engineer

Senior Data Engineer

Senior Data Engineer

Data Protection Engineer

Senior Software Engineer – Data Engineering

AI/ML Engineer, Data Scientist

Senior Director, Data Architect

Senior Data Engineer

Senior Software Engineer, Data