Data Engineer

• Design and implement robust ETL/ELT pipelines
• Build and maintain Feature Stores and specialized datasets
• Develop the data pipelines required for Generative AI
• Act as the technical lead for our Snowflake data warehouse
• Manage complex, cross-platform data workflows using Airflow, Step Functions, or Azure Data Factory
• Partner directly with central IT, Database Administrators, and Security teams
• Implement automated validation and observability layers
• Drive the efficiency of our data stack by optimizing storage
• Work as a dedicated engineering partner to MLOps and Data Science teams

Data Engineer, AI

• Perform Data Warehouse Architecture at an intermediate level
• Design and develop data models and ETL specifications
• Conduct data analysis and recommend solutions
• Participate in data warehouse and dashboard architecture planning
• Propose requirements, standards, and best practices for data modeling and ETL
• Conduct product and vendor research and present recommendations

IS Data Warehouse Architect II

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• Oversight and responsibility for designing, building, and maintaining scalable data pipelines and architectures
• Partner closely with analytics, data science, product, and business teams to ensure high-quality, well-structured data is available for decision-making and operational needs
• Design, build, and maintain scalable data pipelines and ETL/ELT processes
• Develop and optimize data models, data warehouses, and data lakes by working with stakeholders
• Build, optimize, and deploy forecasting models using time-series modeling, machine learning, and MLOps practices
• Apply advanced modeling and statistical analysis to Healthcare Quality metrics spanning NCQA HEDIS measures and CMS Clinical Stars
• Integrate data from multiple sources including internal systems, APIs, and third-party vendors
• Collaborate with analytics, BI, and data science teams to support reporting and advanced analytics

Manager, Data Engineering

• Define and implement enterprise-wide AI data architecture strategies to enable secure, scalable, and compliant AI/ML adoption across the organization.
• Design canonical data models, metadata frameworks, and pipelines to support AI/ML model development, training, deployment, and monitoring.
• Establish standards for data quality, lineage, master data management (MDM), and “golden record” frameworks to ensure reliable AI outputs.
• Collaborate with data governance, compliance, and security teams to enforce policies for ethical and responsible AI data usage.
• Architect and oversee AI-ready data platforms (cloud, hybrid, and on-prem) to integrate structured, unstructured, and streaming data sources.
• Implement modern data architectures (e.g., data mesh, data fabric, Lakehouse, Apache Iceberg/Delta Lake) to accelerate AI/ML projects.
• Optimize AI/ML workloads on cloud platforms (AWS, Azure, GCP) and manage data pipelines using tools like Kafka, Glue, Spark, and Snowflake.
• Partner with AI engineers, data scientists, and business leaders to align data architecture with financial products, regulatory compliance, and risk management needs.
• Provide architectural leadership in modernization efforts (real-time payments, fraud detection, personalization engines, and regulatory reporting).

AI Data Architect

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

• Build, mentor, and empower a high-performing team of data engineers and data professionals, fostering a culture of collaboration, innovation, and continuous growth.
• Lead the design and delivery of modern, scalable data platforms aligned with organizational goals and long-term vision.
• Guide cloud adoption and legacy system migration, including Snowflake (or similar) implementation, ensuring minimal disruption and strong data integrity.
• Oversee the design and operation of batch and real-time data pipelines using modern orchestration and engineering best practices.
• Lead integration across data lakes, warehouses, and access layers to enable seamless data consumption for analytics and business use cases.
• Ensure strong data governance, metadata management, and adherence to security, privacy, and compliance standards.
• Drive adoption of modern engineering practices, including CI/CD, infrastructure as code, and containerization (Docker/Kubernetes).
• Translate leadership vision into actionable plans and deliver measurable outcomes against key business objectives.
• Partner with Data Science, Analytics, and business teams to align priorities and unlock the full value of data.
• Define and implement frameworks, standards, and processes that improve efficiency, scalability, and reliability.
• Ensure all work meets BECU’s compliance, regulatory, and information protection requirements.
• Lead hiring, coaching, and career development efforts to grow strong and future-ready data organization.

Director, Data Engineering

• Collect, manage, and transform large-scale raw data from multiple sources into structured, reliable, and usable formats that meet business and analytical requirements.
• Design, develop, and maintain data architectures, pipelines, and applications using appropriate tools and frameworks to support scalable data processing and product development.
• Build and maintain robust extract, transform, and load (ETL) processes that ensure data accuracy, efficiency, and availability across systems.
• Perform root cause analysis to identify data issues, system inefficiencies, or process gaps, and implement improvements that enhance data quality and operational performance.
• Work in an agile Scrum environment by contributing to sprint planning, development tasks, testing, and sprint reviews to deliver reliable and scalable data solutions

• Design and build ETL/ELT data pipelines to ingest, process, and transform datasets from multiple sources
• Implement best practices for performance tuning, partitioning, and clustering to optimize data queries
• Follow data quality standards, data governance frameworks, and security policies for data storage and access
• Develop and optimize data models and schemas to support analytics, reporting, and machine learning requirements
• Collaborate with data scientists and analysts to design data solutions that integrate with BI tools and machine learning models
• Create comprehensive documentation for data pipelines, workflows, and processes.

• Architect Petabyte Pipelines: Engineer scalable, reliable, and performant data pipelines to assemble large and intricate datasets using SQL, DBT, and Snowflake, ensuring high data availability and integrity.
• Build Data Platforms: Independently design and maintain internal React (TypeScript) interfaces and Python backend services that automate data ingestion and discovery, reducing lead times for application teams from weeks to minutes.
• Develop Data APIs: Build and maintain production-grade REST and gRPC APIs that serve as the high-performance interface between our Snowflake data layer and downstream consumer touchpoints.
• Modernize Data Operations: Implement a GitOps model for data using GitHub Actions and Argo/Kargo, integrating standardized logging, alerting, and automated observability into the heart of all data products.
• Innovate with AI: Leverage Cursor AI, MCPs, and other AI tooling to accelerate the data engineering SDLC, from optimizing complex SQL queries to automating schema migrations.
• Collaborate and Lead: Communicate with business leaders to translate complex data requirements into functional specifications while mentoring other engineers in modern data architecture and software best practices.
• Data Architecture: Design and optimize high-volume ETL/ELT pipelines using SQL, DBT, and Snowflake, ensuring data is modeled for both analytical and operational use cases.
• Internal Tooling (Full Stack): Develop and maintain internal-facing web applications using React that allow data owners to interact with, monitor, and configure their data pipelines.
• API Development: Architect and implement REST and gRPC APIs in Python that serve as the interface between our Snowflake data layer and downstream consumer applications.
• CI/CD & GitOps: Own the deployment lifecycle of data services and tools using GitHub Actions for CI and Argo/Kargo for continuous delivery and lifecycle management.
• Self-Service Platforms: Build "Data-as-a-Service" features, such as automated UI-driven ingestion workflows, reducing the reliance on manual data engineering tickets.
• AI Integration: Utilize modern AI development tools (e.g., Claude AI) to accelerate the development of both data pipelines and management interfaces.

Staff Data Engineer

• 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

• Applies acquired skills, procedures, and decision-making best practices to resolve various issues
• Loads and validates client data into the Clearwater investment account system as the primary responsibility
• Demonstrates a working knowledge of Clearwater Way methodologies to implement and onboard clients, including aligning and supporting CW's Client Engagement Model
• Identifies opportunities for improvement and takes the lead in implementing solutions, both individually and within their role, for continuous service improvement
• Applies their understanding of the accounting equation, amortization, interest income, and other accounting concepts
• Utilizes their understanding of data intake, normalization, reconciliation, and reporting workflow, along with related concepts
• Applies their understanding of mortgages and private equity

Lead Data Migration Specialist

• Applies acquired skills, procedures, and decision-making best practices to resolve various issues
• Loads and validates client data into the Clearwater investment account system as the primary responsibility
• Demonstrates a working knowledge of Clearwater Way methodologies to implement and onboard clients, including aligning and supporting CW's Client Engagement Model
• Identifies opportunities for improvement and takes the lead in implementing solutions, both individually and within their role, for continuous service improvement
• Applies their understanding of the accounting equation, amortization, interest income, and other accounting concepts
• Utilizes their understanding of data intake, normalization, reconciliation, and reporting workflow, along with related concepts
• Applies their understanding of vanilla fixed income and equity investments, comprehending their purpose and functionality within CW
• Understands and applies Clearwater's core value proposition (aggregate, reconcile, report) in a market- and client-agnostic manner
• Coordinates complex activities effectively within a dynamic and agile environment

Senior Data Migration Specialist

• 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

• Design, implement, and operate production data platforms across Azure SQL, on premise SQL Server, Azure Storage, Microsoft Dataverse, and Microsoft Fabric or Power BI.
• Build and maintain ingestion pipelines using SFTP file drops, APIs, CDC or SQL Log Shipping patterns, and Azure Data Factory or equivalent orchestration tools.
• Design and evolve analytic data models using star, snowflake, Data Vault, or hybrid approaches.
• Implement automated data quality checks, validations, and alerts across pipelines and semantic layers.
• Implement practical data governance using Microsoft Purview or equivalent tooling.
• Partner closely with DevOps to reduce manual processes and increase repeatability.

Senior Data Engineer – Architect

• 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

• 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.

Senior Data Engineer

• 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

• Design the vision and blueprint of an organization’s data framework.
• Design and manage data systems, set policies for how data is stored and accessed, coordinate various data sources within an organization, and integrate new data technologies into existing IT infrastructures.
• Data modeling and design, including software development and database administration.

Data Architect – Data Ecosystems

• Build and support scalable cloud data solutions
• Conduct analysis of technical and data requirements
• Design, build, and maintain data pipelines and transformations
• Develop and support data models and curated datasets
• Collaborate effectively with business, analytics, and technical teams

See all jobs on JobTailor

Data Engineer, AI

IS Data Warehouse Architect II

Supply Chain Data Engineer

Manager, Data Engineering

AI Data Architect

Manager, Data Engineering

Director, Data Engineering

Data Engineer

Data Engineer

Staff Data Engineer

Data Engineer

Lead Data Migration Specialist

Senior Data Migration Specialist

Data Engineer

Senior Data Engineer – Architect

Data Engineer – Entry Level

Senior Data Engineer

Staff Software Engineer, Data Platform

Data Architect – Data Ecosystems

Data Engineer III