Senior Software Engineer, Data Platform

Matterworks

Senior Software Engineer building Matterworks’ scalable biological data platform. Designing pipelines, data contracts, enrichment, and quality systems supporting mass-spectrometry research and products.

Posted 8/6/2026full-timeSomerville • Massachusetts • 🇺🇸 United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and scaling data systems and pipelines, with proficiency in Python and SQL for large-scale data processing. Capable of implementing Kubernetes-native orchestration and ensuring high-quality data processing while collaborating with cross-functional teams.

Highest-signal resume keywords

Python ProficiencySQL ProficiencyKubernetes-Native OrchestrationData Pipeline DevelopmentExperience with Modern Data Lake Technologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Pipeline DevelopmentLarge-Scale Data ProcessingKubernetes-Native OrchestrationPythonSQLArgo WorkflowsMetaflowEKSGlueApache Iceberg

Soft Skills

CollaborationProblem-SolvingSkepticism about Output Correctness

Tools & Technologies

TerraformDuckDBAirflowDagsterMzMLRDKitProteoWizard

Industry Keywords

Data ContractsData ServicesQuality ChecksProvenance-Aware Data ProcessingScientific Labels

Tech Stack

Tools & technologies

AirflowApacheKubernetesPythonSQLTerraform

About the role

Key responsibilities & impact

Build and scale data contracts, pipelines, and systems consumed by other teams across multiple petabytes of data
Acquire, store, and serve data efficiently and affordably at scale
Design data layouts and improve featurization throughput
Implement Kubernetes-native orchestration and surface costs proactively
Transform raw data into usable datasets with consistent schemas, trustworthy metadata, and documented definitions
Scale scientific labels from studies to spectra and underlying features
Automate quality checks and publish data only after passing quality gates
Own SDKs and tools used by AI, chemistry, product, and agent interfaces
Operate data services against designed service-level agreements
Ensure high-quality, provenance-aware, and secure data processing
Collaborate daily with machine learning researchers, scientists, and the product team
Report to the Head of Engineering

Requirements

What you’ll need

Significant professional experience building production data systems and pipelines; evaluation based on scope and judgment rather than years
Proficiency in Python and SQL for large-scale data processing
Proficiency in Kubernetes-native batch orchestration and modern data lake technologies, including Argo Workflows, Metaflow, EKS, Glue, Athena, Apache Iceberg, Parquet, DuckDB, and Terraform; Airflow or Dagster experience also accepted
Experience designing stable identifiers for a large, changing corpus
Experience building validation that gates publication rather than reporting issues afterward
Experience putting an LLM or agent component into a production data path, including the evaluation loop, gold set, and cost per record
Daily use of AI coding tools with skepticism about output correctness
Track record of owning work through a running, validated production system, including data fixes, backfills, and debugging failed publishes
Comfort with messy scientific formats and toolchains such as mzML, RDKit, and ProteoWizard or similar
Engineering depth required
Passion for contributing to an early-stage startup and solving novel scientific challenges
Must be currently legally authorized to work in the United States

Benefits

Comp & perks

Stock options
Health and dental benefits
Vision benefits
Long- and short-term disability insurance
Life insurance
401k with company match
Flexible work policy
Unlimited time away policy
Commuter benefits and parking
Regular team meals and outings
Company support for continued education/coursework
Conference participation