Data Engineer – Data Platform & Analytics

Our Client’s data platform processes hundreds of millions of events per day and includes a large-scale data warehouse, batch and streaming pipelines, and modern lakehouse infrastructure. In this role, you will work across the data stack: from building scalable data pipelines and distributed processing systems to designing reliable analytical models and improving their data warehouse architecture.

You will have significant ownership over core data systems and will work closely with Data Scientists, Analysts, Product Managers, and Software Engineers to turn complex business and technical requirements into scalable, production-grade data solutions.

What you'll do

• Design and build scalable batch and streaming data pipelines processing structured and semi-structured data at high volume
• Design and optimize data models and analytical layers supporting reporting, analytics, and machine learning use cases
• Build, maintain, and improve robust ETL/ELT workflows feeding our data warehouse and downstream systems
• Modernize legacy data pipelines and transformation workflows into tested, documented, and maintainable solutions
• Work with large-scale distributed processing technologies such as Spark and Flink
• Build and operate data infrastructure using technologies such as AWS, Kafka, and Apache Iceberg
• Develop production-grade Python services and data tooling with appropriate testing and CI/CD practices
• Optimize data warehouse performance, reliability, and cost
• Establish and improve data quality, testing, freshness checks, and data contracts
• Design dimensional data models, including star schemas and slowly changing dimensions
• Refactor and scale existing pipelines and datasets to support new use cases
• Identify technical risks and drive improvements in platform architecture, reliability, and performance
• Collaborate with Data Scientists, Analysts, Product, and Engineering teams to translate requirements into scalable data solutions
• Participate in architecture and code reviews and contribute to engineering best practices
• Document systems, pipelines, data models, architecture, and operational processes
• Provide technical guidance and mentorship to less experienced engineers

What you bring

• 2-5+ years of hands-on experience in data engineering, analytics engineering, or a related field
• Strong Python and SQL skills, including complex transformations, CTEs, window functions, joins, and query optimization
• Experience building and operating production-grade batch and real-time data pipelines
• Hands-on experience with Spark, Flink, or similar distributed data processing technologies
• Strong understanding of data modeling, ETL/ELT, and data warehouse architecture
• Experience with Redshift, Snowflake, BigQuery, or a similar MPP data warehouse
• Experience with AWS services such as S3, EC2, Lambda, Glue, EMR, Step Functions, or equivalents
• Solid understanding of software engineering practices, including testing, CI/CD, debugging, and production operations
• Experience working with distributed systems and large-scale data processing
• Ability to work independently, take ownership, and make sound technical decisions
• Strong communication skills and the ability to work effectively with both technical and non-technical stakeholders
• Good command of English

Nice to have

• Experience with Kafka and stateful stream processing
• Experience with Apache Iceberg, Delta Lake, or other open table formats
• Experience with lakehouse architectures
• Experience with dbt or a comparable data transformation framework
• Experience migrating or modernizing legacy data warehouses and ETL systems
• Knowledge of data governance, metadata management, lineage, and data quality frameworks
• Experience with Terraform, AWS CDK, or other Infrastructure as Code tools
• Experience building ML training, feature, or inference pipelines
• Experience designing analytical layers or building data platforms from scratch

What they offer