Platform Engineer

Role Overview

We are seeking an experienced Platform Engineer with hands-on expertise in Software Engineering Intelligence (SEI) platforms, engineering data integrations, connector configuration, and platform evaluation .

The candidate will be responsible for configuring and maintaining integrations across Git repositories, work-tracking platforms, and CI/CD systems , ensuring that engineering data is accurately collected, correlated, and made available for analytics and productivity insights.

A key aspect of this role is the ability to perform identity resolution across multiple engineering systems , ensuring that users, teams, repositories, work items, and delivery activities can be accurately associated across disparate data sources. The candidate will also support structured Proofs of Concept (PoCs), data validation, platform evaluations, and integration assessments .

Key Responsibilities

SEI Platform Administration & Engineering

• •Configure, administer, and support Software Engineering Intelligence (SEI) platforms used for engineering productivity and delivery analytics.
• •Manage platform configuration, connectors, integrations, users, teams, and engineering data sources.
• •Work with engineering teams to onboard and configure new data sources and maintain existing integrations.
• •Monitor connector health, data ingestion, synchronization, and platform data quality.
• •Troubleshoot integration, connectivity, authentication, and data ingestion issues.

Connector & Integration Management

• •Configure and maintain connectors across:
• •Git / source-code management platforms
• •Work-tracking systems
• •CI/CD platforms
• •Integrate engineering systems to ensure reliable collection of source-code, work-item, build, deployment, and delivery data.
• •Understand source-system data models and map relevant engineering data into the SEI platform.
• •Validate connector configurations and troubleshoot data synchronization issues.
• •Work with API-based integrations where native connectors are unavailable or require customization.
• •Support onboarding of new engineering teams, repositories, projects, and pipelines into the platform.

Identity Resolution & Data Correlation

• •Implement and maintain identity resolution across multiple engineering systems .
• •Map and reconcile user identities across Git, work-tracking, CI/CD, and other engineering platforms.
• •Investigate duplicate, missing, or incorrectly mapped identities and resolve data inconsistencies.
• •Ensure accurate association of users, teams, repositories, work items, commits, builds, and deployments.
• •Establish processes for maintaining identity mappings as teams, users, and organizational structures change.

Platform Evaluation & Proofs of Concept

• •Support and lead structured Proofs of Concept (PoCs) for SEI and engineering productivity platforms.
• •Define PoC objectives, evaluation criteria, data requirements, and success metrics.
• •Configure connectors and data sources required for platform evaluations.
• •Validate the quality, completeness, accuracy, and usability of platform-generated engineering data.
• •Perform comparative analysis of platform capabilities and data outputs.
• •Document PoC findings, limitations, observations, and recommendations for stakeholders.
• •Work with vendors and internal engineering teams during product evaluations and technical workshops.

Data Validation & Quality

• •Perform structured validation of engineering data collected from source systems.
• •Reconcile data between source systems and SEI platforms to identify discrepancies.
• •Validate metrics related to development activity, work tracking, builds, deployments, and engineering workflows.
• •Investigate data anomalies and work with platform or source-system teams to resolve underlying issues.
• •Establish repeatable data validation and quality-check processes.

AI Coding Assistant Telemetry

• •Support integration and analysis of telemetry from AI-assisted software development tools .
• •Work with engineering productivity platforms to understand usage and activity data generated by tools such as GitHub Copilot, Claude Code, or Cursor .
• •Assist in evaluating how AI coding-assistant telemetry can be incorporated into engineering productivity and developer-experience analytics.
• •Validate telemetry data and ensure appropriate mapping to engineering users, teams, and projects.

Data Platform Integration

• •Support the extraction and downstream consumption of SEI platform data for enterprise analytics.
• •Work with platform exports and APIs to make engineering productivity data available to enterprise data platforms.
• •Support integration of SEI data into a Databricks lakehouse or similar enterprise data environment.
• •Work with Data Engineering teams to validate schemas, data mappings, ingestion processes, and data quality.
• •Document data flows between SEI platforms, engineering systems, and downstream analytics platforms.

Stakeholder Collaboration

• •Collaborate with Engineering, DevOps, Data Engineering, Product, and vendor teams.
• •Gather technical requirements for new integrations and platform capabilities.
• •Communicate integration status, data-quality issues, PoC findings, and platform risks to stakeholders.
• •Support technical demonstrations and platform evaluation sessions.
• •Provide technical documentation covering connectors, integrations, identity mappings, data flows, and operational procedures.

Required Skills & Experience

• •4–8 years of experience in Platform Engineering, DevOps, Engineering Productivity, Software Engineering Intelligence, Data Integration, or a related technical role.
• •Hands-on experience administering or supporting enterprise analytics/productivity platforms .
• •Strong experience configuring and troubleshooting connectors and integrations across:
• •Git/source-code management platforms
• •Work-tracking systems
• •CI/CD platforms
• •Strong understanding of engineering data generated by Git repositories, work-management tools, and CI/CD pipelines.
• •Experience working with APIs and integrations between enterprise platforms.
• •Strong understanding of identity resolution and user mapping across multiple systems .
• •Experience performing structured data validation, reconciliation, and quality analysis .
• •Experience supporting or conducting technology Proofs of Concept (PoCs) and platform evaluations.
• •Ability to analyze platform data and identify inconsistencies, gaps, and integration issues.
• •Strong troubleshooting and problem-solving capabilities.
• •Ability to work with engineering and data teams in a cross-functional enterprise environment.
• •Strong technical documentation and communication skills.

Strongly Preferred Skills

• •Hands-on experience with Software Engineering Intelligence (SEI) platforms such as:
• •BlueOptima
• •DX
• •LinearB
• •Jellyfish
• •Swarmia
• •Harness SEI
• •Experience with GitHub Copilot, Claude Code, Cursor , or similar AI coding assistants and their engineering telemetry.
• •Experience integrating or analyzing AI-assisted development telemetry .
• •Experience working with Databricks and landing/exporting platform data into a lakehouse.
• •Exposure to engineering productivity analytics and Developer Experience (DX) initiatives.
• •Experience with process mining or engineering workflow analytics.
• •Experience with GitHub, GitLab, Azure DevOps, Jira, or similar engineering platforms.
• •Knowledge of SQL and data transformation techniques.
• •Experience with cloud-based data platforms and enterprise data integration patterns.