Senior Data Engineer

SUMMARY OF THE JOB

We are seeking a seasoned Senior Data Engineer to design, build, and optimize our next-generation data platform. You will be responsible for architecting scalable data pipelines, managing large-scale distributed systems, and ensuring our data infrastructure in AWS and Databricks is robust and efficient. The ideal candidate is a Spark expert with a deep understanding of the AWS ecosystem and a passion for automation.

MAIN ACTIVITIES / RESPONSIBILITIES

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Pipeline Architecture: Design and implement complex batch and streaming ETL/ELT pipelines using Python, SQL, and Spark to process massive datasets.

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Cloud Infrastructure: Leverage AWS Data Analytics services to build scalable, secure, and cost-effective data solutions.

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Orchestration & DevOps: Manage and automate data workflows using Airflow, while utilizing Docker and ECS for containerized application deployment.

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System Optimization: Monitor and tune the performance of distributed systems (Spark Cluster) to ensure high availability and low latency.

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Infrastructure as Code: Utilize AWS CloudFormation or Terraform to manage data infrastructure, ensuring repeatable and version-controlled environments.

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Cost Optimization: Monitor and optimize AWS spend by selecting appropriate instance types (Spot vs. On-Demand) and refining data storage strategies.

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Security & Compliance: Implement IAM roles, bucket policies, and encryption (KMS) to ensure data is secure at rest and in transit.

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Collaboration: Work within an Agile framework to deliver iterative value, collaborating closely with Data Scientists and Stakeholders to translate business needs into technical reality.

JOB DIMENSIONS

List of direct reports:

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Up to 2 Direct Reports, and around 15 externals

Key interfaces, stakeholders and relationships:

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Internal:

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GDS: product manager, application manager, data & analytics & AI team

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Country business stakeholders

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External : 3rd party vendors

PROFILE REQUIRED

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Experience: Minimum 4+ years of hands-on experience in active Big Data environments and 2+ years specializing in Data Analytics within AWS.

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Compute & Processing: Amazon EMR: Architecting and managing Spark clusters for large-scale distributed processing.

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AWS Glue: Developing serverless ETL jobs, managing the Data Catalog, and implementing Glue Crawlers.

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Storage & Warehousing:

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Amazon S3: Implementing "Data Lake" best practices, including partitioning, compression (Parquet/Avro), and lifecycle policies.

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Amazon Redshift: Designing star/snowflake schemas and optimizing query performance for high-volume data warehousing.

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Amazon Athena: Performing ad-hoc SQL analysis directly on S3 data.

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Experience with open table formats (iceberg/delta)

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Orchestration & Integration:

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Amazon MWAA (Managed Workflows for Apache Airflow): Deploying and scaling Airflow environments.

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AWS Lambda: Building event-driven data triggers and micro-services.

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Streaming (Advantage): Amazon Kinesis or MSK (Managed Streaming for Kafka) for real-time data ingestion.

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Core Engineering: Expert-level proficiency in Spark, Python, and SQL.

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Infrastructure & Tooling: Proven experience with Airflow for orchestration and Docker/ECS for containerization.

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Good knowledge in Databricks and data mesh architectures. Good understanding in how to implement and maintain Lakehouse data models (bronze / silver / gold layers) using Delta Lake for reliability, ACID transactions, time travel and schema evolution.

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Solid software engineering practices: Git, CI/CD for data pipelines, automated testing, code quality and documentation.

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Communication: Excellent written and oral English communication skills, with the ability to explain complex technical concepts to non-technical audiences.

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Degree in Computer Science, Engineering, Mathematics or related field, or equivalent practical experience.

PREFERRED “PLUS” QUALIFICATION

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Real-time Processing: Experience with streaming and distributed messaging applications like Flink and Kafka.

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Core Tech: Java programming.

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Industrialise ML use cases

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Data Visualization: Experience with QlikView or QlikSense to support BI initiatives.

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Agile: Experience working in a fast-paced Scrum or Kanban environment.

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Certifications: AWS Certified Data Engineer – Associate/Professional or AWS Certified Solutions Architect, Databricks Data engineer (Associated/Professional) certification

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DevOps: Experience with Openshift, Github Actions or Jenkins for CI/CD of data workflows.