Overview
Join to apply for the Senior Data Engineer role at Equisoft .
Equisoft is a global provider of digital solutions for insurance and investment, with a comprehensive ecosystem of scalable solutions to help customers meet challenges of digital transformation. Our team is based in North America, the Caribbean, Latin America, Europe, Africa, Asia and Australia.
Role
The Senior Data Engineer reports to the AVP, Core Insurance and works closely with AI / ML teams, product managers, and software engineering teams. The incumbent will be responsible for designing, building, and maintaining robust data infrastructure and pipelines that support Equisoft's AI and ML initiatives, with a focus on scalable data processing and real-time analytics for insurance and investment solutions.
Responsibilities
- Build and maintain scalable Databricks pipelines for ML workflows, including data ingestion, transformation, and feature engineering for machine learning models
- Implement and optimize synthetic data generation infrastructure to support ML training while ensuring privacy compliance and data quality standards
- Create data augmentation pipelines designed for insurance scenarios, including policy data, claims processing, and risk assessment use cases
- Optimize data storage and retrieval systems for training efficiency, implementing partitioning strategies, caching mechanisms, and performance tuning
- Develop and maintain real-time stream processing capabilities using Apache Spark Structured Streaming, Kafka, and other modern streaming technologies
- Ensure comprehensive data quality and compliance for ML training datasets, implementing validation frameworks, monitoring systems, and governance policies
- Design and implement ETL / ELT pipelines using modern data stack tools including Apache Airflow, dbt, and cloud-native services
- Collaborate with ML teams to establish data versioning, lineage tracking, and reproducibility for model training datasets
- Monitor and troubleshoot data pipeline performance, implementing automated alerting and recovery mechanisms
- Work with cloud platforms (AWS, Azure, GCP) to architect scalable data solutions and optimize costs
- Implement data security best practices including encryption, access controls, and audit logging
Requirements
Technical
Bachelor's Degree in Computer Science, Data, Software, or related field, or College Diploma combined with 4+ years of relevant experienceMinimum of 3 years' experience in data engineering with demonstrated expertise in building production data pipelines with 7 years total in the data fieldExtensive hands-on experience with Databricks platform, including Apache Spark, Delta Lake, and Databricks workflowsProficiency in Python and SQL for data processing, transformation, and pipeline developmentStrong experience with cloud data platforms (AWS, Azure or Google Cloud) and their data services (S3, Redshift, BigQuery, etc.)Experience with real-time stream processing frameworks (Apache Kafka, Spark Structured Streaming, Apache Flink)Knowledge of data orchestration tools such as Apache Airflow, Prefect, or similar workflow management systemsUnderstanding of data modeling concepts, including dimensional modeling, data vault, and lakehouse architectureExperience with infrastructure as code (Terraform, CloudFormation) and containerization (Docker, Kubernetes)Familiarity with version control systems (Git) and CI / CD practices for data pipelinesExcellent knowledge of both French and English (spoken and written)Soft Skills
Strong analytical and problem-solving abilities with attention to detailExcellent communication skills for presenting complex technical concepts to diverse stakeholdersAbility to work effectively in cross-functional teams and manage multiple projects simultaneouslyDetail-oriented approach to data governance, security, and compliance requirementsAdaptability to rapidly evolving data technologies and best practicesSelf-motivated with strong organizational skills and ability to work autonomouslyTeam spirit, collaboration, and knowledge sharing mindsetNice To Haves
Cloud certifications (AWS Data Engineer, Azure Data Engineer, Google Cloud Data Engineer)Databricks certification (Associate or Professional Data Engineer)Knowledge of machine learning workflows and MLOps practicesExperience with data mesh architecture and domain-driven data designExperience with Apache Iceberg, Hudi, or other open table formatsExperience with data quality frameworks (Great Expectations, Deequ, Monte Carlo)Knowledge of privacy-preserving technologies (differential privacy, federated learning)Experience with dbt for analytics engineeringUnderstanding of DataOps practices and data pipeline testing strategiesExperience with insurance or financial services domain and regulatory requirementsEqual Opportunity
Equisoft is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
#J-18808-Ljbffr