Our analytics landscape has grown organically across multiple teams and products. The result is a collection of powerful but siloed tools, each with its own technology stack, data contracts, and deployment patterns. You'll join a team that acts as the connective tissue bringing these applications together, making pragmatic decisions about when to consolidate and when to preserve the right tool for the right job.
This role is for an engineer who is energized by ambiguity, skilled at reading distributed systems, and opinionated (but flexible) about platform design. You will work directly with engineering leads and data teams. As the team's technical lead, you will guide and mentor other engineers while remaining a hands-on contributor.
What You'll Do
Technical Leadership
Set technical direction for a small team of platform engineers, translating requirements and architecture guidance into well-scoped, sequenced work
Mentor engineers through pairing, code review, and design feedback, raising the team's standards for quality, testing, and documentation
Coordinate delivery across contributors, including distributed team members, surfacing risks and dependencies early
Remain a primary hands-on contributor to the team's most complex integration and platform work
Platform Unification & Integration
Audit existing analytics applications to understand their underlying technology choices, data flows, and integration points
Design and implement integration patterns (APIs, event streams, shared data contracts) that allow siloed applications to interoperate without unnecessary rewrites
Build and maintain shared platform services (authentication, observability, configuration management, logging) that reduce duplication across applications
Data Pipeline & ETL Engineering
Develop a deep understanding of existing data pipelines owned by distributed teams, including their data sources, transformation logic, and dependencies
Partner with domain teams who have the deepest knowledge of their own data, acting as an integration architect and collaborator rather than a centralized pipeline owner; identify unification opportunities but defer to domain expertise on data semantics and processing logic
Cloud Infrastructure
Deploy and manage platform services on cloud infrastructure (AWS preferred; Azure/GCP experience a plus)
Contribute to infrastructure-as-code practices using tools such as Terraform or CDK
Collaborate with the IT platform team to define deployment pipelines and reliability standards for integration services
Construct evidence-based business cases for cloud feature adoption, translating technical requirements into business value, risk reduction, and compliance language that resonates with IT governance and procurement stakeholders
What We're Looking For
Required Qualifications
7+ years of software engineering experience, with at least 3 years focused on data engineering, integration, platform, or infrastructure work
2+ years in a technical lead role: setting direction for shared work, mentoring engineers, and reviewing code and designs
Proficiency in Python; comfortable reading and writing production-quality code, not just scripts
Strong SQL skills, including working with large or complex datasets
Hands-on experience with AWS (compute, storage, and at least one managed data service)
Experience with AI-assisted development tools or coding agents in a professional context, and a genuine curiosity about how AI is changing engineering work
Demonstrated ability to learn unfamiliar systems quickly: you can walk into an existing pipeline, tool, or codebase you didn't build and figure out how it works
Strong written communication skills
Preferred Qualifications
Experience with dbt and Snowflake
Experience with Apache Iceberg
Additional language proficiency, especially Spark or Java
Experience leading or coordinating engineering work across distributed or multi-location teams
Experience with data pipelines (ETL/ELT), including schema management or data quality monitoring
Exposure to structured (SQL) and semi-structured (JSON, Parquet, Avro) data at scale
Familiarity with analytics or ML platform tooling (Airflow, MLflow, or SaaS equivalents)
How You Work
We care as much about how you think as what you've built. The ideal candidate for this role:
Is pragmatic over dogmatic: comfortable making a clear recommendation and defending it, but changes course when someone presents a genuinely better argument, not just a louder one
Treats AI-assisted tools as a normal part of how you work, not a novelty, and stays curious about how they change what's possible
Communicates tradeoffs clearly to both technical peers and non-technical stakeholders
Moves quickly from “I don't know this system” to “here's how it works,” and can produce a clear map of an unfamiliar codebase or pipeline
Understands that “don't touch what works” and “build it right” are often both correct answers, depending on context
Takes ownership of quality and reliability: you care about what happens after you ship
Multiplies the team's impact: shares context, unblocks others, and delegates real ownership rather than becoming the bottleneck
The estimated base salary for this job is $140,000 - $190,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $168,000 - $228,000 USD. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.
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- Country
- United States of America
