Data Engineer

• Lead the implementation of AI data pipelines integrating structured, semi-structured, and unstructured data to support AI and agentic solutions
• Develop AI-driven data systems that enhance data capabilities while ensuring adherence to industry best practices
• Implement and optimize Retrieval-Augmented Generation (RAG) architectures and integrate them with enterprise data platforms
• Design, build, and optimize scalable batch and streaming data pipelines with a focus on performance, resiliency, and operational efficiency
• Develop and maintain real-time data streaming pipelines using technologies such as Snowpipe
• Develop data domains and data products to support reporting, analytics, AI/ML, and data science use cases
• Ensure the reliability, availability, and scalability of data pipelines through monitoring, alerting, and incident management
• Implement reliability engineering best practices, including fault tolerance, redundancy, and disaster recovery
• Drive engineering discipline across data platforms, including observability, data quality, lineage, and governance
• Collaborate with DevOps and infrastructure teams to enable seamless deployment and operation of data systems
• Provide architectural leadership in partnership with Data Architects, including defining technical standards and influencing enterprise-wide practices
• Develop and integrate graph database solutions to support complex data relationships within AI systems
• Apply GenAI approaches to insurance-specific data use cases and challenges
• Lead the development of AI-ready data foundations that support scalable, production-grade solutions
• Ensure data platforms remain resilient, governed, and cost-efficient, aligned with enterprise cloud and data strategies
• Mentor junior engineers and contribute to communities of practice, promoting best practices, reusable patterns, and engineering standards
• Stay current with advancements in GenAI and apply relevant technologies and methodologies to platform evolution.

Senior Staff AI Data Engineer

• build and maintain the cloud infrastructure, data pipelines, and deployment workflows
• turn our team's machine learning models into reliable, production-grade services that support STARS ratings
• work across three key areas: cloud data engineering on Google Cloud Platform, backend API development, and DevOps/container orchestration
• collaborate with data scientists, product owners, and business stakeholders to deliver scalable, well-architected solutions that move insights from development to production

Senior Data Engineer, GCP

• Architect and develop robust, scalable ETL/ELT pipelines using Cloud Dataflow, Cloud composer (Airflow), and Pub/Sub for both batch and streaming use cases
• Leverage BigQuery as the central data warehouse and design integrations with other GCP services (e.g., Cloud storage, Cloud functions)
• Build and optimize analytical data models in BigQuery
• Implement partitioning, clustering, and materialized views for performance and cost efficiency
• Ensure compliance with data governance, access controls, and IAM best practices
• Develop integrations with external systems (APIs, flat files etc.) using GCP-native or hybrid approaches
• Utilize tools like Dataflow or custom Python/Java services on Cloud Functions or Cloud Run to handle transformations and ingestion logic
• Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins for deploying data pipeline code and workflows
• Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting to ensure pipeline reliability
• Lead architectural decisions for data platforms and mentor junior engineers on cloud-native data engineering patterns
• Promote best practices for code quality, version control, cost optimization, and data security in a GCP environment
• Drive initiatives around data democratization, including building reusable datasets and data catalogs via Datapelx or Data Catalog

Senior Data Engineer

• Lead the design and stewardship of BSI's data governance architecture, data traceability standards, and quality frameworks, alongside oversight of reference and shared data management — with heightened emphasis on data integrity as a critical enabler of AI/ML model training and regulatory reporting.
• Establish and maintain enterprise standards for data lineage, data quality measurement, and governance processes, developed in close partnership with data governance stakeholders across the organization.
• Responsible for architecting reference and shared data patterns that underpin all business domains — Management Liability, Surety, and Claims — alongside the Analytical domain, ensuring a unified and governed approach to shared data assets across the enterprise.
• Collaborate with Enterprise teams to establish guidelines for AI-ready data, while identifying gaps and defining a clear roadmap to advance BSI's overall data readiness.
• Collaborate closely with the Data Engineering Value Stream to drive data governance implementation and engage with the Enterprise teams to align on and advance cross-enterprise data standards.
• Ensure data architecture decisions support and align with regulatory compliance requirements.
• Partner with senior leaders across Technology and Business to define architecture priorities, align on strategic direction, and ensure architectural decisions are firmly grounded in business imperatives.
• Collaborate with Enterprise architecture and center of excellence teams to define and advance architectural patterns aligned with modern principles; producing best practices, reference materials, and instructional videos that drive consistent adoption across the organization.
• Develop architecture designs and patterns, partnering with engineering teams to ensure effective execution and delivery.
• Build and sustain close working relationships with engineering teams, ensuring the right skills are developed and aligned to support evolving technology direction.
• Champions modern architecture principles and technology modernization, bringing clear thought leadership and a forward-looking perspective to drive meaningful change.
• Proactively identifies opportunities to leverage emerging technology advances by analyzing key industry and technology trends and assessing their potential impact on the enterprise.
• Drive the design and execution of technology proof of concepts, ensuring modern architecture solutions are rigorously assessed for feasibility and fit before implementation.

Data Architect – Data and Analytics

• Provide technical leadership for building and enhancing on‑premises data warehouse solutions
• Accelerate migration to AWS, including Amazon Redshift
• Understand and manage the data needs of stakeholders in a single business vertical
• Responsible for the design and implementation of scalable & fault tolerant data applications
• Partner with information architects, platform architects, data scientists and product management on solution requirements
• Resolve problems through in-depth evaluation of business processes, system processes, enterprise standards & procedures
• Comply with data management policies, standards and procedures
• Lead the technical team to ensure appropriate data solutions to meet business goals
• Engage in mid-level business partner engagement; management of relationship and inter-group planning among technology leadership/peers
• Utilize innovative frameworks to avoid redundancy by promoting automation
• Collaborate with data engineering team members

• Lead the strategy and execution of complex and large Data and Analytics portfolio.
• Ensure data architecture and solutions align with enterprise-wide standards for Data, AI and Analytics.
• Effectively communicate strategy, execution progress, and outcomes to diverse stakeholders and promote data capabilities through thought leadership and presentations.
• Responsible for implementing AI data pipelines that integrate structured, semi-structured, and unstructured data to support AI and Agentic solutions.
• Design, build and maintain scalable real-time data pipelines for efficient ingestion, processing, and delivery.
• Define and operationalize ontologies, context graphs, and knowledge graphs across domains to power reasoning, explainability, and decision intelligence.
• Ensure semantic-first AI and Agentic analytics, ensuring LLMs and agents can consume governed business context, metrics, and rules.
• Drive production-scale execution of semantic and knowledge platforms with strong standards for performance, governance, security, and lifecycle management.
• Identify and champion AI augmented productivity improvements across the end-to-end data management lifecycle.
• Stay current with emerging trends in Agentic AI and data engineering and lead proof-of-concepts and early pilots for emerging data and AI augmented technologies to accelerate speed to market.
• Define and implement robust data management frameworks to ensure successful adoption of Enterprise Data Governance and Data Quality practices.
• Effectively manage the budget and financials for the portfolio.

AVP, AI Data Engineering, Customer Data Ecosystem

• Build and operationalize complex data solutions, correct problems, apply transformations, and recommending data cleansing/quality solutions.
• Design complex data solutions
• Perform analysis of complex sources to determine value and use and recommend data to include in analytical processes.
• Incorporate core data management competencies including data governance, data security and data quality.
• Collaborate within and across teams to support delivery and educate end users on complex data products/analytic environment.
• Perform data and system analysis, assessment and resolution for complex defects and incidents and correct as appropriate.
• Test data movement, transformation code, and data components.
• Perform other duties as assigned.

Data Engineer II – AWS, Databricks, SQL, GenAI

• Provide technical leadership for building and enhancing on‑premises data warehouse solutions and accelerating migration to AWS, including Amazon Redshift.
• Understand and manage the data needs of stakeholders in a single business vertical.
• Responsible for design, development, testing oversight, and production implementation using on‑prem RDBMS platforms, Ab Initio, SQL, and AWS.
• Partner with information architects, platform architects, data scientists, and product management on solution requirements.
• Lead the technical team to ensure appropriate data solutions to meet business goals.

• Design and deliver scalable data pipelines in AWS and Databricks that drive reliable, production-ready data solutions
• Proactively monitor, troubleshoot, and resolve data pipeline issues to ensure consistent system performance and data integrity
• Triage and resolve incidents based on business impact, ensuring timely and effective issue resolution
• Partner with business stakeholders to investigate and explain data discrepancies, improving trust in data assets
• Develop ad hoc analyses and reports using SQL and Python to support critical business decisions
• Automate manual processes to improve efficiency, reduce errors, and accelerate delivery timelines
• Collaborate across engineering, platform, and product teams to manage dependencies and resolve service issues
• Deliver solutions within an Agile (Kanban/DevOps) environment, contributing to continuous improvement and faster delivery cycles

Software Engineering Advisor – Data Engineer

• Provide technical leadership to data engineers, setting standards for solution design, coding practices, data governance, and quality.
• Define and evolve the enterprise data architecture leveraging Snowflake, Data Vault 2.0, and modern event driven and ELT frameworks.
• Lead architecture reviews, establish engineering best practices, and guide platform modernization efforts.
• Mentor engineering team members, facilitate code reviews, and promote continuous learning and innovation.
• Architect and oversee the delivery of scalable data pipelines, ingestion frameworks, and transformation processes using Snowflake, Python, Spark, Informatica (PowerCenter/IDMC), AWS Glue, and cloud-native tooling.
• Design and maintain enterprise data models, including Data Vault 2.0 (Hubs, Links, Satellites, PIT, Bridge structures) and dimensional models.
• Develop and optimize Snowflake platform capabilities, including: Snowpipe, Streams, Tasks, File Formats, External Stages Dynamic Tables and ELT pipeline automation.
• Warehouse sizing, performance tuning, and cost optimization.
• Implement scalable batch, micro-batch, and real-time ingestion solutions following best practices.
• Translate complex business requirements into technical design specifications and actionable development plans.
• Partner with product owners, business stakeholders, architects, and analytics teams to deliver high-impact data solutions.
• Manage technical execution across multiple initiatives, ensuring alignment with enterprise priorities, data strategies, and delivery timelines.
• Oversee documentation, deployment readiness, testing processes, and quality assurance for production releases.
• Ensure operational reliability, data accuracy, and performance of enterprise data warehouse and analytical environments.
• Lead root-cause analysis for production issues and drive implementation of long-term preventative solutions.
• Conduct performance optimization on Snowflake workloads, SQL queries, pipelines, and integrations.

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• Design, develop, and maintain scalable ETL/ELT pipelines for high-volume batch and real-time data
• Build and optimize analytics infrastructure that supports developer productivity and engineering metrics
• Partner with cross-functional teams to deliver data products and self-service insights
• Develop and maintain data models, schemas, and architectures for modern data platforms
• Ensure reliability, performance, and data quality across pipelines and platforms
• Contribute to DevOps and CI/CD practices for data and backend services
• Document designs, workflows, and best practices to support team knowledge sharing

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Build Pipelines: Design and maintain scalable data pipelines to ingest and enrich healthcare data using Databricks and Spark.
• Optimize Data Workflows: Improve data intake processes and optimize SparkSQL/Python workloads for performance, scalability, and cost efficiency.
• Design Data Models: Partner with senior engineers to develop data marts and semantic models that power analytics products and reporting.
• Ensure Quality: Monitor pipeline health, troubleshoot failures, and implement data validation and quality controls.
• Learn & Grow: Expand your knowledge of the full data lifecycle, cloud infrastructure (Azure/AWS), and healthcare data standards.

Data Engineer – Entry Level

• Build and optimize analytical data models in BigQuery
• Implement partitioning, clustering, and materialized views for performance and cost efficiency
• Ensure compliance with data governance, access controls, and IAM best practices
• Develop integrations with external systems using GCP-native or hybrid approaches
• Utilize tools like Dataflow or custom Python/Java services on Cloud Functions or Cloud Run
• Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins
• Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• Contribute to the development of data pipelines, APIs, and services supporting AI and analytics platforms
• Assist in building and maintaining cloud-based data workflows for structured and unstructured data
• Write clean, well-documented, and testable code following established standards
• Support AI platforms such as: Synthetic data generation components (Deep Synthetic Foundry)
• Implement clearly defined features or enhancements under guidance from senior engineers
• Work with SQL and cloud data platforms (e.g., Snowflake) to ingest, transform, and validate data
• Ensure data quality, reliability, and performance in production pipelines
• Partner with Data Scientists, Engineers, and Product team members on shared goals

Junior Developer – Data Engineer, AI Platforms

• You will work closely with finance, accounting, billing, and revenue teams to understand their main pain points and translate them into self-serve, reliable, and scalable data solutions
• You are expected to mentor other team members and be a champion of engineering excellence across the organization
• You will be part of a small team with a large amount of ownership and autonomy, managing initiatives directly from design through execution
• You will have the freedom to suggest and drive organization-wide initiatives that shape the financial data vision and roadmap
• You will own critical data integration pipelines and models, ensuring uniform, reliable, timely, and accurate financial reporting

Senior Data Engineer, Finance

• Architect and develop robust, scalable ETL/ELT pipelines using Cloud Dataflow, Cloud composer (Airflow), and Pub/Sub for both batch and streaming use cases.
• Leverage BigQuery as the central data warehouse and design integrations with other GCP services (e.g., Cloud storage, Cloud functions).
• Build and optimize analytical data models in BigQuery.
• Implement partitioning, clustering, and materialized views for performance and cost efficiency.
• Ensure compliance with data governance, access controls, and IAM best practices.
• Develop integrations with external systems (APIs, flat files etc.) using GCP-native or hybrid approaches.
• Utilize tools like Dataflow or custom Python/Java services on Cloud Functions or Cloud Run to handle transformations and ingestion logic.
• Build automated CI/CD pipeline using Cloud Build, GitHub Actions, or Jenkins for deploying data pipeline code and workflows.
• Set up observability using Cloud Monitoring, Cloud Logging, and Error Reporting to ensure pipeline reliability.
• Lead architectural decisions for data platforms and mentor junior engineers on cloud-native data engineering patterns.
• Promote best practices for code quality, version control, cost optimization, and data security in a GCP environment.
• Drive initiatives around data democratization, including building reusable datasets and data catalogs via Datapelx or Data Catalog.

See all jobs on JobTailor

Senior Staff AI Data Engineer

Senior Data Engineer, GCP

Senior Data Engineer

Data Architect – Data and Analytics

Senior Data Engineer

AVP, AI Data Engineering, Customer Data Ecosystem

Data Engineer II – AWS, Databricks, SQL, GenAI

Senior Data Engineer

Software Engineering Advisor – Data Engineer

Senior Data Engineer

Data Engineer

Senior Data Engineer

Data Engineer

Data Engineer – Entry Level

Data Engineer

Senior Data Engineer

Staff Software Engineer, Data Platform

Junior Developer – Data Engineer, AI Platforms

Senior Data Engineer, Finance

Senior Data Engineer