Data Engineer

• Design, build, and operate batch and streaming data pipelines on modern cloud data platforms.
• Develop robust ETL/ELT processes using SQL, Python, and PySpark.
• Implement layered / medallion data architectures and analytics-ready data models.
• Lead the implementation and evolution of data governance practices.
• Define and operationalize governance standards for data products and AI/ML features.
• Design and build AI agents and intelligent automation workflows.

Senior Data Engineer, Governance Focus

• 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

• Designing and developing modern, data-centric applications that support clinical and operational workflows
• Building scalable data pipelines, transformation and enrichment processes, and intuitive user interfaces
• Mentoring junior Data Engineers

Data Engineer I

• Join Intel's Corporate Planning Capabilities team in supporting the biggest transformation in our company's history.
• Play a pivotal role in shaping the way SAP product master data is organized, managed, and utilized across the enterprise.
• Be the trusted subject matter expert, driving innovation and excellence in SAP master data architecture and analytics.
• Empower Intel to make informed, data-driven decisions, enabling the company to remain at the forefront of technological advancement.
• Lead transformative projects that enhance data efficiency, scalability, and compliance, contributing to our mission of delivering world-class solutions.

SAP Master Data Architect

• Design, build, and maintain scalable data pipelines that support continuous data flow, analytics, and machine learning use cases
• Develop and implement data models and schemas to ensure data integrity, accessibility, and usability
• Manage and optimize relational and NoSQL database systems for performance and availability
• Integrate data from multiple sources while ensuring consistency, quality, and reliability
• Design and manage ETL processes to move and transform data across systems
• Implement data governance practices to support data security, privacy, and compliance
• Collaborate with data scientists, machine learning engineers, analysts, platform engineers, and business stakeholders to understand requirements and deliver effective solutions
• Tune database and pipeline performance for efficient processing and querying of large datasets
• Maintain clear documentation for data processes, models, architecture, and operational workflows
• Partner with internal platform teams to build reliable, scalable, and reusable data solutions

• Apply knowledge of health care and database management to analyze clinical data, and to identify and report trends.
• Design and build modern data-centric software applications to support clinical and operational processes across all parts of the healthcare system.
• Work closely with the Product, Platform, and Architecture teams to deliver on joint efforts.
• Build data pipelines and transformations, data enrichment processes, provisioning layers, and user interfaces to meet the requirements of key initiatives.
• Mentor and assist less experienced Data Engineers and participate in research and development of new technologies to help set up repeatable processes or templates for other Data Engineers to follow.

Senior Data Engineer IS

• Data Ingestion: Design and implement scalable data pipelines for ingesting and processing telemetry, logs and sensor data from our fleet of robots and associated cloud software.
• Multimodal Data Integration: Develop systems to handle complex multimodal logs, ensuring precise synchronization between video, audio, IMU, and joint-state data for downstream analysis and robot 'black box' reconstruction/playback.
• Edge Data Agents: Contribute to the development of data ingestion agents running on-robot to manage high-volume data throughput in variable connectivity environments.
• Data Optimization: Implement and optimize data formats (e.g., MCAP, Parquet) specifically tuned for the storage and rapid querying of rich multimodal robotics recordings.
• Observability systems: Develop and maintain observability systems using tools like fluentbit, OpenTelemetry etc enabling real-time monitoring and analytics of fleet operations.
• Distributed Processing: Use Spark, Kafka, or Flink to transform raw robot data into structured datasets ready for business intelligence and long-horizon fleet analytics.
• Stakeholder Collaboration: Work closely with AI and Hardware teams to ensure data tools meet the needs of those training the next generation of humanoid models.
• Cloud Platform: Build and optimize tools for internal stakeholders for business intelligence and long-horizon fleet analytics and forecasting using existing tools like AWS S3, Athena, Sagemaker etc.

Software Engineer III, Data Platform

• 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

• Designs and builds modern data-centric software applications for healthcare
• Builds data pipelines and transformations, and user interfaces
• Mentors less experienced Data Engineers
• Collaborates with Product, Platform, and Architecture teams

Senior Data Engineer – Cloud Hosting

• 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

• 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

• 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

• Architect event-driven pipelines (Kafka) and develop new data models to ensure transactional integrity for commercial events
• Automate scalable ETL processes and refactor next-generation data architectures for quality and security
• Collaborate across teams to codify business processes into self-measuring systems, debugging complex challenges

Senior Systems Data Engineer, Financial Transactions, Automation

• 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

• 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

• Apply knowledge of health care and database management to analyze clinical data, and to identify and report trends.
• Design and build modern data-centric software applications to support clinical and operational processes across all parts of the healthcare system.
• Build data pipelines and transformations, data enrichment processes, provisioning layers, and user interfaces to meet the requirements of key initiatives.
• Mentor and assist less experienced Data Engineers.
• Participate in research and development of new technologies to help set up repeatable processes or templates for other Data Engineers to follow.
• Collaborate with Product, Platform, and Architecture teams to deliver on joint efforts.

• Management of Cogito suite of reporting tools
• Cogito Security management
• Maintenance of Cogito Related System Wide Settings
• Coordinates Analytics Processes/build across all Epic Applications
• Oversite of Cogito Tools build and provisioning standards
• Coordinating, auditing, troubleshooting technical system functionality – including reporting queues
• Represent Cogito in Epic governance groups
• Coordination & Management of Cogito related projects – including Epic Releases

Data Engineer II

• Define and evolve CFC-wide data architecture across all major systems and platforms
• Establish clear data domains, system boundaries, and integration patterns
• Assess current and future organizational data needs and translate them into architectural direction
• Develop long-term data architecture patterns that balance scalability, maintainability, and delivery constraints
• Design and maintain conceptual, logical, and physical data models representing CFC’s core entities, relationships, and measures
• Define, document, and govern source system schemas including canonical fields, data types, naming standards, and versioning approaches
• Establish systems of record and systems of reference for key domains such as accounts, licenses, users, and programs
• Ensure consistent implementation of data models across operational systems
• Create clear documentation that enables shared understanding across technical and non-technical stakeholders
• Architect and guide data flows between CFC systems to support operations, analytics, research, and decision-making
• Design integration and transformation approaches that preserve semantic meaning
• Oversee large-scale data integration, transformation, and storage patterns for reliable analytics
• Monitor and evolve data flows to reduce duplication, fragility, and unnecessary complexity
• Embed privacy, security, and compliance requirements directly into data models and integrations
• Partner with IT, security, and legal teams to align architecture with regulatory and policy requirements
• Ensure privacy and security protocols, including field-level encryption, role-based access control, deletion, and auditing are architected into and consistently supported across CFC’s data architecture
• Define data quality standards and validation strategies, including management of schema drift over time
• Enablement & Collaboration
• Work closely with engineers, analysts, researchers, data stewards, and business partners
• Serve as a subject-matter expert on CFC’s data architecture
• Communicate complex data concepts clearly to diverse audiences

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Senior Data Engineer, Governance Focus

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Supply Chain Data Engineer

Data Engineer I

SAP Master Data Architect

Data Engineer

Senior Data Engineer IS

Software Engineer III, Data Platform

Director, Data Engineering

Senior Data Engineer – Cloud Hosting

Data Engineer

Data Engineer

Data Engineer – Entry Level

Senior Data Engineer

Senior Systems Data Engineer, Financial Transactions, Automation

Staff Software Engineer, Data Platform

Senior Data Engineer, Finance

Senior Data Engineer

Data Engineer II

Principal Data Architect