The Lead Data Engineer will be responsible for architecting, building, and optimizing the data pipelines and infrastructure that power International SOS's mission-critical analytics and operational platforms. This role plays a pivotal part in transforming raw data into structured, reliable, and scalable formats to enable data-driven decision-making across the organization. As a technical leader, you will guide engineering best practices, mentor junior team members, and collaborate closely with cross-functional stakeholders to deliver high-impact data solutions.

Position – how you’ll contribute

Design, construct, install, and maintain scalable and robust data pipelines
Integrate data from a wide variety of sources, both structured and unstructured
Optimize data delivery and re-design infrastructure for greater scalability and performance
Collaborate with data scientists, business analysts, and product managers to deliver data solutions aligned with business needs
Develop and maintain data models, databases, and ETL processes
Ensure data quality, reliability, and integrity across systems
Participate in data architecture and design reviews
Assist in implementing data governance and data security best practices
Monitor and troubleshoot data pipeline issues and performance bottlenecks

Qualifications

Expectations – the experience you need:

A bachelor’s degree in computer science, data science, software engineering, information systems, or related quantitative field; master’s degree preferred
At least eight years of work experience in data management disciplines, including data integration, modeling, optimization and data quality, or other areas directly relevant to data engineering responsibilities and tasks
Proven project experience developing and maintaining data warehouses in big data solutions.

Experience & Technical Expertise

8+ years of hands-on experience in data engineering or related roles, with a strong foundation in building scalable data solutions.
Proficient in designing and orchestrating ETL/ELT pipelines across structured and unstructured data sources using tools like AWS Glue, Apache Airflow and DBT
Deep expertise in big data technologies such as Apache Spark, Kafka, Hadoop, and EMR.
Strong programming skills in Python, Scala, Java, and familiarity with .NET as needed.
Skilled in working with relational (SQL, Postgres) and NoSQL (DynamoDB) databases.

Cloud & Data Platforms

Proven experience with cloud platforms including AWS (S3, Lambda, Redshift, Glue, Step Functions).
Hands-on knowledge of modern data warehousing solutions such as Redshift, BigQuery, and Teradata.

Data Governance & Security

Solid understanding of data privacy regulations (e.g., GDPR, HIPAA) and security best practices.
Experience implementing data governance frameworks, metadata management, and data quality controls.

Analytical & Business Acumen

Ability to design and deploy data solutions that support AI/ML, business intelligence, and advanced analytics use cases.
Familiarity with data science tools visualization as MS Fabric, Power BI and Excel.
Strong problem-solving and debugging skills, with the ability to identify and resolve performance bottlenecks in complex systems.
Familiarity with data science tools and libraries such as Pandas, NumPy, Scikit-learn, Jupyter, and R.

Collaboration & Communication

Experience working in agile environments and cross-functional teams
Excellent interpersonal and communication skills; able to work effectively across technical and non-technical teams.
Capable of translating business requirements into technical solutions and vice versa, bridging gaps between executives, analysts, and engineering teams.
Demonstrated ability to influence senior stakeholders and contribute to strategic data initiatives