Staff Software Engineer – Data Acquisition
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 12d ago
The date the source published, not the day we noticed it (2026-10-21). Last seen at its source just now.
We have tracked this listing since 21 Oct 2026 (11 days). The employer's own board has carried it every time we have read it, most recently just now.
Is it remote?
US - Remote
That is the location the employer filed this posting under. Quoted as written — we do not re-word the source's own location.
Who may apply?
United States
The description states no restriction of its own. This is the source's own tag.
Pay
$140.6k–173.1k/yr
Read out of the job description by us, not from a structured field. Shown in the posting's own currency and period; we never convert.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Workday employer's own board first seen 11d ago · last seen just now
The listing
This is a remote position; however, the candidate must reside within 30 miles of one of the following locations: Portland, ME; Boston, MA; Chicago, IL; Dallas, TX; San Francisco Bay Area, CA; and Seattle/WA.
About the Role
The Data Acquisition Team is the entry point to WEX’s Data-as-a-Service (DaaS) platform—responsible for ingesting, validating, and orchestrating raw data from dozens of internal systems and third-party providers.
As a Staff Software Engineer, you will own the technical direction and delivery of core data acquisition platform capabilities. You will remain hands-on while leading architecture decisions, developing reusable ingestion services, and guiding engineers across teams. Your work will support batch, change data capture (CDC), and event-driven ingestion, with clear expectations for data quality, reliability, security, and cost.
WEX is undergoing a data platform transformation—and this team builds the foundation. Every pipeline you create contributes directly to powering analytics, automation, and product intelligence across all business domains.
If you’re passionate about scaling data platforms from the ground up, this is your chance to help shape how WEX ingests and leverages its most valuable asset: data.
What You’ll Do
- Build and maintain high-performance data acquisition pipelines that integrate with a variety of systems across WEX and third-party vendors.
- Lead the design and delivery of a modular ingestion platform with reusable connectors and configuration-driven onboarding. Establish metadata and configuration patterns that reduce source-specific development and support Data Acquisition as a Service.
- Build schema validation, completeness checks, reconciliation, duplicate prevention, and audit logging into shared ingestion components.
- Support safe retries, replay, backfills, and schema evolution so failures can be recovered without losing or incorrectly duplicating data.
- Design batch, CDC, and streaming ingestion patterns that meet agreed freshness, throughput, and recovery requirements. Evaluate performance, source-system impact, and operating cost when selecting and extending ingestion technologies.
- Ensure traceability, lineage, and security through integration with control plane and governance systems.
- Lead design and code reviews, maintain technical specifications and architectural decisions, and mentor engineers in reusable platform design.
- Establish automated testing and CI/CD practices for ingestion code, connector configuration, and infrastructure, with validation before production deployment.
- Partner with the established production support process on complex escalations, root-cause analysis, and preventive engineering improvements.
What You Bring
Required:
- B.Sc. in Computer Science, Engineering, or related technical field; M.Sc. a plus but not required.
- 8+ years of software engineering experience, including delivery of production distributed systems or data platforms and demonstrated technical leadership across team boundaries.
- Strong programming skills in Python, Java, Scala, or another systems-focused language.
- Experience with data integration and ingestion pipelines—you understand what it means to bring data in from source systems, transform it, and deliver it reliably downstream.
- Solid grasp of engineering fundamentals, including version control, modular design, testing, and performance tuning.
- Demonstrated experience designing and operating production integrations that handle schema evolution, failure recovery, observability, and secure access.
- Ability to lead architecture decisions, explain engineering tradeoffs, resolve dependencies across teams, and guide implementation through production adoption.
- Practical experience using AI-assisted engineering tools for design, coding, testing, or documentation, with validation of generated outputs for correctness, security, and maintainability.
- A collaborative mindset—comfortable working across domains, products, and infrastructure layers.
- A strong sense of ownership and accountability—you care deeply about building systems that last.
Preferred:
- Experience building reusable connectors, metadata-driven ingestion services, or configuration-driven onboarding capabilities.
What Success Looks Like
During your first year, you will work with engineering and product leadership to establish baselines and agree on outcomes such as:
- Reducing onboarding lead time and manual effort through shared connectors and configuration-driven patterns.
- Increasing adoption of reusable ingestion capabilities across sources and teams.
- Meeting agreed completeness and freshness criteria, with reliable reconciliation and recovery.
- Improving deployment quality and reducing recurring failures through automated testing and actionable observability.