Sr Data Engineer

Build and support enterprise-class data pipelines and scalable cloud data solutions in a hybrid role in Washington state.

Responsibilities

• Design and develop complex multi-tier systems covering analysis, coding, testing, debugging, and documentation for cloud-native data solutions and integration services
• Coordinate, support, and execute design and development of complex data workflows, including batch, near real-time, and API-based integrations
• Design and support prototypes, proofs of concept, and scalable data solutions that align technical delivery with business needs
• Support metadata management, master data policies, data quality standards, lineage, and governance practices within data workflows and pipelines
• Organize and guide data curation policies across data platforms and workflows based on technical requirements
• Support, mentor, and coordinate small to medium-sized teams through design reviews, code reviews, testing, and development activities
• Partner with Architects, Product Owners, and leadership teams to document technology roadmaps, cloud modernization initiatives, and future-state data platform strategies
• Contribute to coding standards, CI/CD practices, automated testing, and code quality initiatives while shaping and improving engineering best practices
• Support monitoring and observability capabilities to improve pipeline performance, reliability, and operational outcomes
• Use automation and AI-assisted development tools, including GitHub Copilot and related technologies, to improve productivity and code quality
• Collaborate with architecture, governance, security, and platform engineering teams to deliver secure, scalable, reusable data products and services
• Present technical topics to engineering audiences and contribute to growing data engineering capabilities across the organization
• Perform responsibilities in line with BECU Competencies, compliance, regulatory, and Information Protection requirements

Requirements

• Bachelor’s degree in computer science or related discipline, or equivalent work experience
• Minimum 5 years of functional experience in data engineering
• Minimum 4 years coding experience with Python, Scala, Java, and SQL
• Demonstrated experience with cloud technologies including Azure, AWS, and GCP
• Demonstrated experience with modern data platforms including Azure Synapse, Azure Databricks, Redshift, Big Query, and Snowflake
• Demonstrated experience with ETL/ELT, SQL, and cloud data integration tools, including Azure Data Factory, Azure SQL, Cosmos DB, Event Hubs, Azure Storage, Azure functions, Container apps, and API Management
• Demonstrated experience with SQL, data modeling, data warehousing, data analysis, and supporting enterprise-scale operational data stores (ODS), data warehouses, and data lake or Lakehouse solutions
• Experience with CI/CD systems and tools such as Azure DevOps and GitHub
• Experience with Git, including code reviews, pull requests, and branching standards such as Git Flow or Trunk-Based Development
• Knowledge of financial services concepts, regulations, and data domains including banking, lending, deposits, payments, and risk management
• Experience building AI-ready data products and supporting AI agent architectures
• Experience with GitHub Copilot or AI-assisted Azure OpenAI, Azure AI Search, Copilot Studio, AI Foundry, MCP integrations, or related AI technologies
• Previous experience with master data (MDM), metadata, data quality, data lineage, data cataloging, and data governance tools including Alation, Erwin, Collibra, Azure Purview, Infosphere, and Informatica MDM
• Experience with Test Driven Development concepts, methods, and tools, including unit testing, integration testing, or performance/load testing
• Proven ability to deliver highly scalable data solutions across the data lifecycle from ideation to retirement
• Proven ability to stay current on emerging technologies and new applications of existing data technologies through work or continuing industry or education involvement
• Proven experience leading and collaborating across teams and business units to deliver solutions through the SDLC
• Experience presenting to technically adept audiences

Technologies

• Python, Scala, Java, SQL
• Azure, AWS, GCP
• Azure Synapse, Azure Databricks, Redshift, Big Query, Snowflake
• Azure Data Factory, Azure SQL, Cosmos DB, Event Hubs, Azure Storage, Azure functions, Container apps, API Management
• Azure DevOps, GitHub, Git
• Git Flow, Trunk-Based Development
• GitHub Copilot, Azure OpenAI, Azure AI Search, Copilot Studio, AI Foundry, MCP
• Alation, Erwin, Collibra, Azure Purview, Infosphere, Informatica MDM
• ETL, ELT, CI/CD

Benefits

• 401(k) Company Match (up to 3%)
• 4% annual contribution to your 401(k) by BECU
• Medical, Dental and Vision (family contributions as well)
• PTO Program + Exchange Program
• Tuition Reimbursement Program
• BECU Cares volunteer time off + donation match

Pay Range

• Target Pay Range: $141,800.00 - $173,300.00 annually
• Full Pay Range: $110,100.00 - $204,900.00 annually
• Additional compensation incentives may be available for the hired applicant
• Incentives are performance based and targets vary by role

Residency / Location Requirements

• Candidates must be residents of WA, OR, ID, AZ, TX, GA, SC, NC, CA, or VA
• If located in Washington state and within a reasonable driving distance from Tukwila, come into HQ on Tuesdays & Wednesdays
• For candidates outside the commute distance of TFC in approved remote work locations, this role is remote
• Remote or onsite, engagement and inclusion in a collaborative environment are emphasized

What You’ll Gain

• Influence the design and evolution of modern cloud-based data platforms at enterprise scale
• Exposure to cutting-edge technologies across cloud, data engineering, automation, governance, and AI-enabled solutions
• Opportunities to mentor and develop other engineers while contributing to technical excellence and continuous improvement
• Collaboration with cross-functional teams to deliver secure, scalable, reusable data products that support business outcomes
• Build solutions that support analytics, operational decision-making, and future AI-driven capabilities