Staff Software Engineer, Data

Jobgether; United States; On-site; USD 176,000 - 230,000; Full time

Benefits offered by this job

Annual base salary

Equity

Health insurance

HSA and FSA

Life insurance

Disability coverage

401(k) match

Parental leave

Flexible time off

Work equipment

Transparent compensation

Job summary

Jobgether is seeking a Staff Software Engineer, Data in the United States to own and evolve a scalable data platform supporting operations and AI research. You will design and operate data infrastructure, connect business systems, and transform complex data into reliable insights.

You will build large pipelines converting raw and synthetic media into high‑quality training data across audio, video, image, and 3D modalities.

Qualifications

• 6+ years building and operating production data platforms.
• Experience integrating SaaS systems (Stripe, HubSpot, ERP) into a data warehouse.
• Experience with multimodal training datasets and batch workloads on HPC or cloud schedulers.
• Expert SQL and Python with automated testing and CI/CD.
• Hands-on with data lakes and warehouses (Iceberg/Delta Lake on S3, Athena/Trino, Snowflake/BigQuery, dbt).
• Experience with dbt, Airflow, Argo, Spark, and distributed processing tech.
• Strong knowledge of AWS, Kubernetes, EKS, Terraform; autonomous in small teams.

Responsibilities

• Own data platform end-to-end: cloud infra, integrations, warehouse modeling, and pipelines.
• Build integrations across billing, CRM, ERP, identity, and telemetry systems.
• Develop warehouse models, data contracts, quality tests, lineage, and catalog docs.
• Implement PII access controls and data classification with Security and Legal teams.
• Process billions of telemetry records and terabytes of logs; optimize Iceberg tables and costs.
• Manage orchestration on Kubernetes/EKS with Helm, Terraform, CI/CD, monitoring, alerts.
• Participate in on-call rotation and troubleshoot production data pipelines.
• Deliver finance leadership reports on revenue, credits, and cloud spend.
• Support research teams in sourcing and versioning large training datasets.
• Build pipelines for media generation and captioning; create data shards for SLURM and cloud storage.
• Maintain dataset lineage, quality metrics, and documentation for reproducibility.
• Lead design reviews, mentor teammates, and develop runbooks for resilience.

Skills

SQL

Python

Data modeling

Airflow

Spark

Kubernetes

AWS

dbt

ETL pipelines

SQL tuning

Tools

dbt

Airflow

Argo

Spark

Iceberg/Delta Lake

S3

HPC/Cloud schedulers

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Software Engineer, Data based in the United States.

We are looking for an experienced data engineer to own and evolve a comprehensive data platform supporting business operations and advanced AI research. In this hands‑on role, you will design and operate scalable data infrastructure, connect business systems, and transform complex data into reliable, actionable insights. You will also build large‑scale pipelines that convert raw and synthetic media into high‑quality training datasets across audio, video, image, and 3D modalities. Working closely with Finance, Product, Security, Infrastructure, Marketing, and Research teams, you will help establish reliable data flows and robust governance practices. You will have significant technical ownership in a small, collaborative environment that values engineering judgment, autonomy, and measurable outcomes. This opportunity is ideal for a senior engineer who combines strong software development practices with deep data modeling expertise and an interest in enabling cutting‑edge generative AI research.

Accountabilities

You will own the data platform end to end, from cloud infrastructure and business‑system integrations to warehouse modeling and multimodal research pipelines, ensuring data remains reliable, secure, scalable, and accessible.

• Own and maintain the company data platform, including the AWS data lake built with S3, Athena, and Glue, the dbt transformation layer, data ingestion pipelines, and the PostgreSQL serving layer used by dashboards and AI agents.
• Build and maintain integrations across billing, customer relationship management (CRM), enterprise resource planning (ERP), identity, and telemetry systems, ensuring revenue, usage, cloud costs, and customer identities reconcile automatically.
• Develop and maintain warehouse models, dimensional schemas, product event data contracts, data quality tests, lineage tracking, and data catalog documentation.
• Implement and enforce personally identifiable information (PII) access controls and data classification practices, including open and restricted schemas, in collaboration with Security and Legal teams.
• Process large‑scale datasets involving billions of telemetry records and terabyte‑scale log streams, optimizing Iceberg tables, partitioning strategies, query performance, and infrastructure costs.
• Manage data orchestration and infrastructure on Kubernetes and Amazon EKS, using Helm, Terraform, CI/CD pipelines, monitoring, and alerting to ensure reliable daily operations.
• Participate in the business‑hours on‑call rotation and troubleshoot failures affecting production data pipelines.
• Deliver operational reporting for Finance and leadership, including revenue by business line, credits and prepaid liabilities, and cloud spending by department.
• Support research teams across audio, video, image, and 3D modalities by sourcing, verifying licenses for, ingesting, and versioning large training datasets.
• Build scalable pipelines for media generation, filtering, deduplication, and captioning, producing training‑ready data shards across SLURM clusters and cloud object storage.
• Maintain dataset lineage, quality metrics, and documentation so researchers can reproduce training datasets consistently.
• Lead technical design reviews, mentor teammates, and develop runbooks that improve operational resilience and reduce reliance on individual contributors.
• Collaborate with Finance, Marketing, Site Reliability Engineering (SRE), Security, Product, and Research on cross‑functional data initiatives.

Requirements

The ideal candidate combines strong software engineering discipline, advanced data modeling skills, production infrastructure expertise, and the ability to work independently while taking ownership of complex technical systems.

• At least 6 years of experience building and operating production data platforms, including responsibility for architectural decisions.
• Demonstrated experience integrating SaaS business systems, such as Stripe, HubSpot, ERP platforms, and identity providers, into a centralized data warehouse.
• Production experience preparing large‑scale multimodal training datasets and running batch workloads using high‑performance computing (HPC) or cloud schedulers such as SLURM, Kubernetes, or equivalent technologies.
• Expert‑level SQL and Python skills, supported by strong engineering practices including automated testing, code reviews, and continuous integration.
• Hands‑on experience with data warehouses and data lakes, including data modeling and technologies such as Apache Iceberg or Delta Lake on S3, Athena or Trino, Snowflake or BigQuery, and dbt.
• Experience operating production orchestration systems such as dbt, Airflow, or Argo, and working with distributed processing technologies such as Spark or equivalent frameworks.
• Strong knowledge of AWS, Kubernetes and EKS, Helm, and Terraform, with the ability to troubleshoot issues across data processing jobs and infrastructure deployments.
• Ability to work autonomously in a small team, make sound technical decisions, and take end‑to‑end responsibility for production systems.
• Strong analytical judgment and the ability to interpret data accurately, communicate its limitations, and ensure stakeholders understand what metrics do and do not represent.

Additional Strengths That Would Be Valuable Include

• Experience owning and building a data platform from the ground up in a startup environment.
• Experience collaborating with research teams working on generative AI models or handling audio, video, image, and 3D datasets.
• Familiarity with FinOps practices and cloud cost allocation and reporting.
• Knowledge of SOC 2 or similar compliance frameworks governing data access and security.

Benefits

• Annual base salary: $175,500–$230,000 USD for eligible candidates hired in the United States, depending on relevant experience, skills, and interview performance.
• Equity: Participation in the company equity program for eligible employees.
• Health insurance: Medical coverage for employees and their families, with PPO and HMO options. Selected plans include 100% premium coverage for eligible employees and dependents.
• HSA and FSA accounts: Options to set aside pre‑tax funds for healthcare and other eligible expenses.
• Life insurance: Company‑paid life insurance, with optional additional coverage for employees, spouses, and children.
• Disability coverage: Short‑term and long‑term disability benefits to help protect income in the event of illness or injury.
• Retirement savings: 401(k) plan with a company match of up to 4%.
• Paid parental leave: Support for employees as their families grow.
• Flexible time off: Flexible paid time off and paid company holidays.
• Work equipment: Equipment provided to help employees perform their work effectively.
• Transparent compensation: Open discussions about salary ranges, compensation decisions, and potential leveling throughout the recruitment process.

How Jobgether Works

We use an AI‑powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top‑fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice:

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre‑contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.