Senior Software Engineer, Data ProductsFull-Time | Remote, Greater Boston Area PreferredOverviewIntePros is seeking a
Senior Software Engineer, Data Products to join a growing technology organization building large-scale data products that make complex web, behavioral, and account-level data actionable for business users.
This is a highly hands-on engineering role with significant ownership. You will design solutions, write the code, deploy it, monitor it in production, and troubleshoot it when something goes wrong. The environment is built around small, autonomous engineering teams where senior engineers are expected to make technical decisions and take responsibility for the systems and data products they build.
The ideal candidate is a strong
Python engineer with deep experience building and operating production data systems, ETL pipelines, cloud data platforms, and event-driven applications in AWS.
What You'll Do- Own data products and processes end-to-end, including design, development, testing, deployment, monitoring, and production support.
- Build and maintain large-scale ETL and data-processing pipelines handling web, behavioral, and account data.
- Develop both scheduled and event-driven batch processing solutions.
- Take direct ownership of production systems, including deploying your own work and resolving issues when they occur.
- Ensure the accuracy, reliability, and integrity of data produced by your systems.
- Identify and resolve silent data-quality issues in addition to traditional application or infrastructure failures.
- Make key technical and architectural decisions with a focus on simple, scalable solutions.
- Write meaningful automated tests that validate real business logic.
- Provide substantive code reviews and contribute to engineering standards.
- Own and improve infrastructure, CI/CD, and observability associated with the team's products.
- Help consolidate legacy systems and processes onto the organization's current technology platform.
- Partner closely with Data Science, Product, and Engineering stakeholders to translate requirements into production solutions.
- Communicate technical decisions and tradeoffs clearly to both technical and non-technical partners.
What We're Looking For- Senior-level software engineering experience building and operating production systems.
- Demonstrated ability to own products or services end-to-end with limited supervision.
- Strong Python development experience across data processing and service/API environments.
- Strong SQL skills and experience with cloud data warehouses such as Snowflake.
- Experience building reliable ETL and large-scale data-processing pipelines.
- Strong understanding of data correctness, idempotency, testing, and production reliability.
- Hands-on experience with event-driven architecture in AWS.
- Experience with AWS technologies including SQS/SNS, S3, IAM, and Secrets Manager.
- Experience deploying workloads using Docker and Kubernetes.
- Experience with workflow orchestration technologies such as Argo Workflows, Airflow, Dagster, or similar.
- Strong operational mindset with experience instrumenting, monitoring, and troubleshooting your own production systems.
- Ability to work effectively within a small, highly autonomous engineering team.
- Ability to explain technical decisions and tradeoffs to Product and Data Science stakeholders.
Nice to Have- Infrastructure-as-Code experience using Pulumi or Terraform.
- Experience deploying or supporting machine learning models in production.
- Exposure to transformer inference, vector databases, GPU scheduling, or LLM APIs.
- Experience modernizing or consolidating legacy data platforms.
Technical EnvironmentLanguages: Python, SQL
Cloud: AWS
Data: Snowflake, large-scale ETL and data processing
Architecture: Event-driven systems, SQS/SNS, S3
Containers: Docker, Kubernetes
Orchestration: Argo Workflows, Airflow, Dagster
Infrastructure as Code: Pulumi, Terraform
AI/ML: LLM APIs, transformer inference, vector databases, GPU workloads
The EnvironmentThis is a good fit for an engineer who wants
real ownership rather than a narrow development lane. Engineers work in small teams, ship frequently, operate what they build, and are expected to understand how their technical decisions affect the reliability and correctness of the overall data product.
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