Integration Engineer

Company: Mediamint

Website: Visit Website

Business Type: Startup

Company Type: Product & Service

Business Model: B2B

Funding Stage: Pre-seed

Industry: Software Development

Salary Range: ₹ 12-15 Lacs PA

Job Description

What do we do?

MediaMint is an AI-powered Revenue Operations services company dedicated to accelerating innovation and

revenue growth for platforms and publishers worldwide. Our mission is to enhance operational efficiency,

scalability, customer satisfaction, and data-driven insights through advanced technology, automation, and AI

capabilities. We serve industry leaders across AdTech, Consumer, CTV, Publishing, and Retail.

Connect Client Systems To MediaMint AI Platform

MediaMint is evolving from best‐in‐class growth services for global leaders into an AI‐first organization. At the

center is our self‐service platform of modular agents that compress hours of advertising, marketing, and

sales‐ops work into minutes. We’re hiring a Client Integration Engineer to make our platform flawlessly in each

client’s stack—securely connecting data, workflows, and identity so impact shows up in days, not months.

The Opportunity

You’ll own the technical path from discovery to go‐live for client integrations: APIs, data pipelines, secure auth,

and eventing that power Mia’s agents. You’ll also build reusable connectors and playbooks that scale across

150+ global accounts, partnering closely with Product, Service Delivery, and Platform Engineering.

Responsibilities

• Integration Ownership – Translate business workflows into technical designs; implement and validate integrations across REST/GraphQL APIs, webhooks, event streams, and bulk data jobs.
• Reusable Connectors & SDKs – Build and maintain MediaMint AI connectors (auth, pagination, rate‐limit handling, retries, schema mapping) and light SDKs/templates to accelerate future work.
• Data Onboarding & Modeling – Map and normalize client data (campaigns, audiences, creatives, spend, outcomes) into MediaMint AI’s canonical schemas; add validation, dedup, and lineage checks.
• Identity & Access – Implement SSO/SAML/OAuth2/SCIM; manage secrets and least‐privilege IAM; ensure tenancy isolation and secure data paths (VPC peering, PrivateLink/Private Service Connect).
• Eventing & Automatons – Wire streaming/event‐driven flows (Kafka/Pub/Sub/Kinesis), idempotent processors, retries, and dead‐letter queues for resilient automations.
• Quality & Observability – Instrument integrations with logs/metrics/traces; define SLIs/SLOs; author runbooks and escalation paths; lead blameless RCAs and preventative fixes.
• Security & Compliance – Embed privacy by design (PII handling, minimization, consent, retention) aligned to SOC 2, ISO 27001, and GDPR/CCPA.
• Partnering & Solutioning – Join discovery/presales to scope feasibility, draft high‐level designs, estimate effort/risks, and set clear expectations.
  
  

• 5+ years in Integration, Solutions, or Platform/Backend Engineering delivering production‐grade integrations for SaaS or data platforms.
• Strong in APIs (REST/GraphQL/gRPC), webhooks, and auth flows (OAuth 2.0, SAML, SCIM); hands‐on secret management.
• Proficient in Python (preferred) or TypeScript/Go; comfortable with Linux, containers, and CI/CD.
• Cloud experience in AWS/GCP/Azure (networking, IAM, VPC peering, private connectivity, cost/throughput trade‐offs).
• Built data pipelines (batch & streaming) with Airflow/Cloud Composer, dbt, Kafka/Pub/Sub/Kinesis; warehouses such as Snowflake/BigQuery/Redshift.
• Knowledge of contracts & change management (OpenAPI/Swagger, versioning, backward compatibility, feature flags, blue‐green/canary).
• Evidence of debugging and performance tuning across API limits, serialization, async queues, concurrency, and storage layers.
• Clear written and verbal communication with both technical and non‐technical stakeholders.
  
  

• Domain familiarity with AdTech/MarTech/RevOps data models (campaigns, pacing, attribution, audiences, product feeds).
• Exposure to agentic/AI workflows (prompt orchestration, evaluation, feedback signals) and tooling such as MLflow, Vertex AI, or Bedrock.
• Experience with multi‐tenant design and tenant‐aware observability, throttling, and isolation.
• Contributions to integration playbooks, sample apps, or open‐source connectors.