inDrive

Global ride-hailing platform with fare negotiation

Senior Data Engineer

Data EngineeringUpdated on Nov 13

Full-timeCairo, Cairo Governorate, EgyptNo salary listedHybrid

About the Job

Requirements

Strong practical Python skills, including writing clean, well-structured, tested code for services, tooling, and data pipelines.
Solid software design skills, including object-oriented programming, modularity, and design patterns.
Experience building and operating services in a cloud environment such as GCP or AWS, including CI/CD, containerization, monitoring, and alerting.
Familiarity with Kubernetes and Terraform.
Hands-on experience with data warehouse tasks using BigQuery or another cloud warehouse, and confidence working with SQL.
Ability to communicate clearly with non-engineering stakeholders, including analysts and business teams.
Ability to take ownership of technologies or services and proactively contribute ideas to the team.

Responsibilities

Build and operate batch and streaming ingestion into a layered BigQuery data warehouse using Airflow, Debezium change data capture over Kafka with protobuf, Pub/Sub, and Dataflow.
Integrate external data sources end-to-end, including marketing platforms, payment providers, S3 buckets, and third-party APIs, handling schema contracts, backfills, and reconciliation.
Engineer the data platform in Python by developing custom Airflow operators and connectors in a shared ETL framework, Kafka Connect on Strimzi and Kubernetes, Cloud Functions, and external-provider API integrations.
Build CI/CD and change-management tooling for BigQuery, including GitHub-based test-and-approval flows, SQL migration engines, sandbox validation, backup, and rollback.
Own pipeline reliability and correctness through idempotency, deduplication, late-data handling, backfill and replay, freshness monitoring and alerting, and integration and unit testing.
Drive data governance and compliance, including ITGC-compliant BigQuery change management, IAM and least-privilege access, PII policy tags and DLP, Unity Catalog on Databricks, column-level lineage using OpenMetadata or Dataplex, and disaster-recovery planning.
Build internal data tools and platform services for agentic data workflows, including Streamlit apps, Slack bots, LLM-based agents, and MCP servers to help teams find and use data.
Support analysts and business teams with data requests and foster data-driven decision-making across the company.
Contribute to system design and architecture with the development team.

Desired Qualifications

Advanced SQL skills, including complex queries, window functions, partitioning, clustering, and cost optimization.
Experience building reliable pipelines around change data capture, such as Debezium, including idempotency, schema evolution, backfills, and reconciliation.
Analytical data modeling skills, including table grain, facts versus dimensions, slowly changing dimensions, and metric definitions.
Experience with stream processing frameworks such as Flink or Apache Beam/Dataflow.
Exposure to data governance and audit compliance, including ITGC/SOX, Databricks Unity Catalog, or lineage/catalog tooling such as OpenMetadata or Dataplex.
Interest in building LLM-based agents and AI tooling for data.

We are looking for a Senior Data Engineer to join one of the Data Platform teams that works with the Marketing, Growth, partner, and financial data domains.
You will be working with cutting edge cloud technologies (GCP, AWS, BigQuery, Databricks, K8s) and building a large scale data infrastructure for analytics, machine learning, and streaming/CDC data delivery.

Build and operate batch and streaming ingestion into a layered BigQuery DWH (raw → ODS → data marts) using Airflow, Debezium CDC over Kafka with protobuf, Pub/Sub, and Dataflow
Integrate external data sources end-to-end — marketing platforms (GA4, AppsFlyer, TikTok/Meta/Google Ads), payment providers, S3 buckets, and third-party APIs — including schema contracts, backfills, and reconciliation
Engineer the data platform itself in Python: custom Airflow operators and connectors in a shared ETL framework, Kafka Connect on Strimzi (K8s), Cloud Functions, and API integrations with external providers
Build CI/CD and change-management tooling for BigQuery: GitHub-based test-and-approval flows, SQL migration engines (Liquibase/Flyway/Bytebase), sandbox validation, backup and rollback
Own reliability and correctness of pipelines: idempotency, deduplication, late-data handling, backfill and replay, freshness monitoring and alerting; write integration and unit tests
Drive data governance and compliance: ITGC-compliant change management for BigQuery, IAM and least-privilege access, PII policy tags and DLP, Unity Catalog on Databricks, column-level lineage (OpenMetadata/Dataplex), and disaster-recovery planning
Build internal data tools and platform services for agentic workflows with data — Streamlit apps, Slack bots, LLM-based agents and MCP servers that help teams find and use data
Support analysts and business teams with data requests, fostering data-driven decision-making across the company
Contribute to system design and architecture with the development team

Strong practical Python: clean, well-structured, and tested code for services, tooling, and data pipelines
Solid software design skills (OOP, modularity, design patterns) — we build platform tools for agentic workflows with data and plan to develop data-related backend services, so well-designed code is highly valued
Experience building and operating services in a cloud environment (GCP, AWS or similar): CI/CD, containerization, monitoring and alerting
Familiarity with Kubernetes and Terraform — our infrastructure runs on GCP/K8s
Hands-on experience with DWH-related tasks (BigQuery or another cloud warehouse) and confident working SQL
Clear communication with non-engineering stakeholders — a meaningful share of the work is data requests from analysts and business teams
Demonstrated ability to take ownership of technologies or services and proactively contribute ideas to the team

Nice to have

Advanced SQL: complex queries, window functions, partitioning, clustering, and cost optimization
Experience building reliable pipelines around CDC (e.g., Debezium): idempotency, schema evolution, backfills, and reconciliation
Analytical data modeling skills: table grain, facts vs dimensions, slowly changing dimensions, and metric definitions
Experience with stream processing frameworks such as Flink or Apache Beam/Dataflow
Exposure to data governance and audit compliance (ITGC/SOX), Databricks Unity Catalog, or lineage/catalog tooling (OpenMetadata, Dataplex)
Interest in building LLM-based agents and AI tooling for data

About the Company

inDrive

Global ride-hailing platform with fare negotiation

inDrive operates a global mobility and urban services platform that connects passengers with independent drivers through a fare-negotiation system. Riders propose a trip price, and nearby drivers can accept, decline, or counteroffer, creating a peer-to-peer ride-hailing model. The service has expanded to intercity rides, delivery and cargo, groceries, advertising, and driver-focused fintech within its app. Unlike typical ride-hailing apps that use fixed fares, inDrive enables price negotiation and direct driver-rider interaction, and it pursues growth through a venture arm that funds ride-hailing-adjacent startups in emerging markets. This broader strategy aims to build a super-app for urban and gig-economy needs, with the goal of offering a wide range of mobility and urban services in 48 countries.

Company size: 10,001+
Company stage: Debt Financing
Total funding: $532M
Headquarters: Mountain View, CA
Founded: 2012

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