Key skills
Data EngineeringAWS S3AWS EC2AWS ECSAWS EKSAWS AthenaAWS RedshiftAWS GlueAWS Step FunctionsTerraformCloudFormationSQLRelational Database DesignData WarehousingLakehouse SnowflakeLakehouse Databricks Delta LakeGraph Databases Neo4jGraph Databases AWS NeptunePythonpandasPySparkTypeScriptNode.jsC#Machine Learning Data PreparationFeature StoresVector Databases Postgres pgvectorVector Databases ChromaVector Databases PineconeLangChainLlamaIndexDockerKubernetesCI/CD PipelinesAirflowPrefectdbtData Compliance and Security (SEC regulated environment)AIMachine LearningMLLLMVector DatabaseETLELTData LakeAnalyticsSnowflakeRedshiftDatabricksKafkaAWSECSEKSEC2S3GlueAthenaKinesisCI/CDCommunicationCollaboration
About this role
Allocate is transforming private market investing by enabling RIAs and family offices to seamlessly discover, model, and manage their private market exposure. They are seeking a Senior Data Engineer to help build out the data infrastructure that powers analytics, reporting, and data-driven product features.
Responsibilities:
- Build and Extend Data Architecture: Build on and extend Allocate's data lakehouse on AWS, combining data lake storage and warehouse technologies to store diverse financial datasets. Contribute to our knowledge graph that models key relationships (investors, funds, companies, etc.) and to the vector database integration that stores embeddings for semantic search and retrieval across our AI agents, models, and providers
- Develop Data Pipelines: Create robust ETL/ELT pipelines to ingest, clean, and transform data from various sources (internal application data and third-party APIs). Ensure both batch processing and real-time data streaming are handled to support up-to-date analytics and recommendations. Build pipelines with an eye on scalability (able to handle increasing data volume and complexity) and reliability (proper error handling and monitoring)
- Enable AI/ML Capabilities: Work closely with our data science and engineering team to provision the data and infrastructure needed for machine learning models and AI features. This includes preparing training datasets, setting up feature stores, and orchestrating workflows that feed LLM-based agents with the context they need (e.g. retrieving relevant data via vector similarity search). You will also help implement systems to serve AI model outputs (such as recommendations) back into the product in real time
- Engineering Excellence and Collaboration: Partner with our data lead and the broader engineering team to deliver data and AI infrastructure. Raise the bar through thoughtful code review, testing, and adherence to best practices, and help engineers who consume data in their services do so effectively. Work in cross-functional squads to incorporate data-driven features into the product roadmap, and share your expertise with peers as the team grows
- Infrastructure and DevOps: Collaborate with our DevOps engineers to deploy and maintain data services. Containerize and orchestrate data tools (using Docker/Kubernetes on AWS EKS) for production use. Implement CI/CD pipelines for data workflows so that changes to data processing or models are tested and deployed automatically. Monitor the health and performance of our data platforms (setting up alerts, dashboards) and be ready to troubleshoot and resolve issues in production to ensure uptime of critical data and AI services
- Continuous Improvement: Stay up to date with the latest in data engineering and AI, from new AWS offerings to open-source ML tools. Evaluate and recommend new technologies, for example assessing whether a stream processing platform like Kafka/Kinesis or an orchestration tool like Airflow could improve pipeline reliability. Challenge conventions and innovate: we encourage rethinking how things are done as we push to build a world-class, intelligent platform
Requirements:
- 5+ years of hands-on experience in data engineering (or related fields), including designing and building large-scale data pipelines and storage solutions
- Strong experience working with AWS cloud services for data, including tools like S3, EC2, ECS, EKS, Athena, Redshift, Glue, and Step Functions
- Proficiency in SQL and relational database design
- Experience building or working with data warehouses or lakehouses (e.g. Snowflake, Databricks Delta Lake)
- Fluency in at least one major programming language used in data engineering, such as Python
- Understanding of how machine learning models consume data
- Solid understanding of containerization and deployment
- Ability to analyze complex data problems, debug pipeline issues, and optimize system performance
- Experience working in a regulated SEC environment, handling sensitive investor/financial data
- Excellent communication skills and a collaborative mindset
- Bachelor's degree in Computer Science, similar technical field of study, or equivalent practical experience