Senior Data Engineer

Recruiting a Senior Data Engineer requires a clear understanding of the role. The following description is a general framework and should be adapted to your organization’s specific context.

The Senior Data Engineer is a technical expert responsible for designing, developing, and optimizing complex data pipelines and infrastructures. They play a key role in transforming raw data into actionable assets, ensuring reliability, performance, and scalability. Working closely with data scientists, data analysts, and IT teams, they support analytics, machine learning, and business process automation initiatives. Their expertise enables them to solve complex technical problems while mentoring junior team members.

Responsibilities and Missions

1. Design and Develop Advanced Data Pipelines

Design modern data architectures (data lakes, data warehouses, NoSQL databases) aligned with business and technical needs.
Build robust and scalable ETL/ELT pipelines with tools such as Apache Spark, Airflow, dbt, Kafka.
Optimize pipeline performance (parallelization, partitioning, caching).
Automate data ingestion and transformation from multiple sources (ERP, CRM, APIs, IoT) to analytics and AI platforms.

2. Ensure Data Quality and Reliability

Implement quality controls (schema validation, anomaly detection, cleaning).
Document pipelines and processes for easier maintenance and knowledge transfer.
Work with data stewards to ensure compliance with governance and business rules.
Apply automated testing to validate data integrity and quality.

3. Maintain and Improve Data Infrastructures

Deploy and configure solutions on AWS, Azure, GCP or on-premise, ensuring scalability and security.
Monitor pipeline performance and troubleshoot incidents proactively.
Migrate legacy systems to modern platforms (Snowflake, Databricks).
Automate repetitive tasks (deployments, updates) using Terraform, Docker, Kubernetes.

4. Collaborate with Data Science and Analytics Teams

Deliver optimized datasets for machine learning (feature engineering, bias management).
Support model industrialization through MLOps practices.
Provide APIs or data views for easy access by BI tools (Tableau, Power BI).
Translate business requirements into technical solutions.

5. Mentor and Train Junior Team Members

Guide junior engineers on problem-solving and best practices.
Conduct code reviews to improve maintainability and quality.
Share expertise via workshops, documentation, and pair programming.
Contribute to recruitment and onboarding of new engineers.

6. Innovate and Continuously Improve Processes

Research and integrate emerging technologies (streaming, data mesh, lakehouse).
Optimize infrastructure costs through performance tuning and resource efficiency.
Contribute to the company’s technical roadmap with practical improvements.
Participate in innovation projects (e.g., generative AI, real-time data solutions).

Examples of Concrete Achievements

Built a real-time pipeline with Kafka & Spark, reducing reporting delays from 24h to 1h.
Migrated batch processes to streaming, improving dashboard responsiveness by 60%.
Optimized SQL queries, cutting execution times by 80% and reducing server load.
Automated unstructured data processing (logs, PDFs), eliminating 90% of manual errors.
Mentored 3 junior engineers, boosting team productivity by 25%.