Senior Data Engineer
Janus Henderson · Denver
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- Senior Data Engineer
- City: Denver
- Division: Technology Delivery
Why work for us?
A career at Janus Henderson is more than a job, it’s about investing in a brighter future together.
Our Mission at Janus Henderson is to help clients define and achieve superior financial outcomes through differentiated insights, disciplined investments, and world-class service. We will do this by protecting and growing our core business, amplifying our strengths and diversifying where we have the right.
Our Values are key to driving our success, and are at the heart of everything we do:
Clients Come First - Always | Execution Supersedes Intention | Together We Win | Diversity Improves Results | Truth Builds Trust
If our mission, values, and purpose align with your own, we would love to hear from you!
Your opportunity
As a Senior Data Engineer, you will own the technical direction for a subsystem or cross-product concern within the Janus Henderson Data Platform. You will act as a go-to engineer for architectural decisions in your area, designing resilient data products and platform capabilities that span multiple business domains. The role requires deep technical craft, strong domain awareness, applied AI fluency, and the ability to influence engineering standards across the Data Engineering guild.
- Own design and delivery of complex data engineering capabilities across subsystems or cross-product concerns, setting technical direction within your area.
- Design systems that span multiple products or domains, such as entitlement models, concordance frameworks, semantic layers, ingestion frameworks or reusable platform services.
- Provide deep technical expertise in at least one core platform pillar, including Snowflake internals, dbt architecture, orchestration, CDC/streaming or equivalent platform capabilities.
- Build reusable engineering assets, including dbt macros, custom materialisations, Python packages, ingestion frameworks, MCP tooling or other shared libraries where the standard toolkit does not fit.
- Diagnose and resolve performance, reliability and cost issues at query-plan, pipeline and platform level.
- Assess architectural trade-offs, including build-versus-buy decisions and second-order impacts across downstream reporting, analytics, operations and regulatory processes.
- Design and deliver production-grade AI-enabled tooling where appropriate, including agents, retrieval pipelines, MCP servers or other applied AI capabilities with appropriate guardrails for a regulated environment.
- Own quality gates, observability and incident learning for your area, including postmortems, root cause analysis and continuous improvement actions.
- Mentor junior engineers and data engineers, run design reviews and establish standards that other engineers can adopt consistently.
- Communicate technical trade-offs clearly to architecture, product, operations, compliance and other non-technical stakeholders.
- DevOps and Source Control Work with fellow team members to collaborate and review source code. Deep understanding of branching strategies and able to release production quality code through change control processes.
- Carry out other duties as assigned
What to expect when you join our firm
- Hybrid working and reasonable accommodations
- Generous Holiday policies
- Excellent Health and Wellbeing benefits including corporate membership to Wellhub
- Paid volunteer time to step away from your desk and into the community
- Support to grow through professional development courses, tuition/qualification reimbursement and more
- Maternal/paternal leave benefits and family services
- Unique employee events and programs including a 14er challenge
- Complimentary beverages, snacks and all employee Happy Hours
Must have skills
- Deep expertise in at least one data platform pillar, such as Snowflake internals, dbt architecture, orchestration, CDC/streaming or distributed data processing.
- Advanced SQL skills, including query-plan analysis, performance tuning, cost optimisation and troubleshooting on cloud data platforms.
- Strong Python engineering capability, with experience building reusable frameworks, packages or libraries rather than only one-off scripts.
- Proven ability to design systems that span multiple products, domains or platform concerns, with awareness of downstream impacts and operational risk.
- Experience making technical trade-offs under delivery pressure, including scope, quality, build-versus-buy and maintainability decisions.
- Strong experiences with DevOps, branching strategies, Azure Portal/Keyvault/Appreg concept.
- Experience with Microsoft Azure services, such as Azure Data Factory, Azure Key Vault and Azure DevOps, for CI/CD and infrastructure integration.
- Working knowledge of financial services data domains, with the ability to understand hand-offs between Investments, Distribution, Operations, Regulatory, Corporate and Finance processes.
- Ability to write clear design documentation, present trade-offs to non-technical stakeholders and influence engineering standards beyond your immediate product area.
- Experience with production-grade AI-enabled tooling, such as agents, RAG/retrieval pipelines, MCP servers or AI-assisted engineering workflows.
- Must be able to use vscode copilot for development work
- Experience mentoring engineers, leading design reviews and supporting technical decision-making across a team or guild.
Nice to have skills
- Experience with dbt Core/Cloud, including custom macros, packages, tests, contracts or materialisations.
- Experience designing or operating semantic layers, entitlement engines, concordance models, data contracts or reusable platform services.
- Understanding of AI/LLM risk in a regulated environment, including data egress, auditability, model non-determinism and appropriate guardrails.
- Knowledge of data governance frameworks, lineag
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