Data Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Data Engineer TLV Bringg processes over 200 million orders a year through infrastructure that some of the world's largest retailers depend on daily. When the data pipeline works, deliveries land on time at scale. When it doesn't, customers feel it within the hour. We're looking for a Data Engineer to own and evolve the data infrastructure that sits underneath all of it. The pipeline is already built and running at real scale. Your job is to go deeper - embedding data further into the business and pushing the AI/ML layer forward. This isn't a maintenance role. It's an ownership role. In this role, you will: - Our data pipelines run faster, scale cleaner, and break less - because you own the architecture and optimization of our BigQuery warehouse end-to-end. - High-throughput processing and real-time analytics become possible at a scale we haven't reached yet - because you're shaping the distributed systems that get us there. - Data capabilities land in the hands of the people who need them - data scientists, engineers, and product stakeholders from problem to solution, not as a downstream dependency. - The infrastructure gets more reliable, more automated, and easier to monitor - because you treat DevOps and MLOps as part of the job, not someone else's problem. - AI/ML models move from development into production and stay there - not handed off, but owned through the full deployment lifecycle. What you Bringg Must have: - 4+ years building high-scale data pipelines and managing cloud data

Read the full description at job-boards.greenhouse.io. FewerJobs shows a preview and links to the original posting.