Head of Data Engineering

Recruiting a Head of Data Engineering requires a thorough understanding of the role. The following is a generic summary and should be adapted to your specific context.

The Head of Data Engineering is responsible for designing, developing, and maintaining data infrastructures and pipelines that enable the company to fully leverage its data assets. In a data-driven transformation context, their role is to build robust, scalable, and high-performance foundations to power analytics platforms, AI, and business applications.

They work closely with Data Science, Data Governance, IT, and business teams to ensure data is accessible, reliable, and actionable in real time or batch, while adhering to security and compliance standards.
Goal: industrialize data collection, storage, processing, and distribution to enable informed decision-making, process automation, and large-scale AI deployment.

Responsibilities and Missions

1. Design and Maintain Data Architecture

• Define and optimize the technical architecture of data platforms (data lakes, data warehouses, ETL/ELT pipelines).

• Select appropriate technologies (e.g., Databricks, Snowflake, Kafka, Apache Spark, Airflow, dbt).

• Architect pipelines to ingest, transform, and distribute data from sources (ERP, CRM, IoT) to end users.

• Ensure scalability and performance, anticipating future needs (AI, advanced analytics).

2. Industrialize Data Collection and Processing

• Automate data flows from source systems to target platforms.

• Develop robust pipelines to clean, enrich, and aggregate data.

• Implement monitoring mechanisms to detect and resolve anomalies (delays, errors, quality issues).

• Optimize processing (parallelization, partitioning) to reduce latency and costs.

3. Ensure Data Quality, Security, and Compliance

• Guarantee clean, consistent, and up-to-date data in collaboration with data stewards.

• Enforce security measures (encryption, controlled access) and compliance with regulations (GDPR, ISO 27001).

• Document metadata (lineage, data dictionary) for transparency and usability.

• Integrate automated quality and validation tests into pipelines.

4. Collaborate with Data Science and Analytics Teams

• Provide ready-to-use datasets optimized for analysis and machine learning.

• Support industrialization of AI models through MLOps (MLflow, Kubeflow).

• Align infrastructures with business use cases (reporting, forecasting, automation).

5. Drive Adoption of Best Practices and Innovation

• Promote best practices (DataOps, DevOps for data).

• Train and mentor engineers on new technologies (streaming, graph databases, generative AI).

• Experiment with emerging approaches (data mesh, lakehouse, low-code).

• Measure impact of infrastructures (processing time, costs, user satisfaction).

6. Align Data Engineering with the Global Strategy

• Prioritize data projects based on business value and ROI.

• Integrate data pipelines into the broader IT and business ecosystem.

• Contribute to the technology roadmap to support ambitions (AI, monetization, innovation).

Examples of Concrete Achievements

• Built a data lake on AWS with automated pipelines, reducing processing time by 50%.

• Deployed a data mesh architecture to decentralize governance and improve agility.

• Industrialized IoT ingestion with Kafka and Spark, reducing errors by 90%.

• Implemented a data catalog (Collibra, Alation) with a business glossary, boosting adoption by 40%.

• Automated AI model deployment with MLOps pipelines, cutting time-to-production by 60%.