Data Platform Engineer - Scalable Pipelines & Observability

Parallel Web Systems; New York (NY); On-site; USD 170,000 - 230,000; Full time

Benefits offered by this job

Competitive salary

Generous equity

Visa sponsorships

401K plans

Daily lunch & office snacks

Dinner at the office

Unlimited vacation

Caltrain pass reimbursement

Job summary

Parallel Web Systems is building the data foundations powering model training, product development, and analytics. You will design data pipelines, craft scalable storage and query serving layers, and implement quality, lineage, and observability systems to ensure trustworthy data across teams.

You will reason about trade-offs between batch and streaming, storage versus query performance, and DIY vs off-the-shelf infrastructure, contributing to a reliable platform used by numerous internal

Qualifications

• Have deep intuition on distributed data processing, data modeling, and system reliability.
• You like to reason about trade-offs between batch and streaming, storage cost vs query performance, and build-it-yourself vs off-the-shelf infrastructure.
• Focus on data correctness and building dependable systems used across the company.

Responsibilities

• Design pipelines that ingest and transform data at scale.
• Build storage and serving layers that are fast and reliable to query.
• Establish quality, lineage, and observability systems for trusted data.
• Anticipate scaling bottlenecks and make architectural calls to keep the platform ahead of demand.

Skills

Distributed data processing

Data modeling

System reliability

Education

Bachelor's degree or equivalent

Job description

Parallel Web Systems is building the data foundations powering model training, product development, and analytics. You will design data pipelines, craft scalable storage and query serving layers, and implement quality, lineage, and observability systems to ensure trustworthy data across teams.

You will reason about trade-offs between batch and streaming, storage versus query performance, and DIY vs off-the-shelf infrastructure, contributing to a reliable platform used by numerous internal