Our client is a technology-driven financial services firm serving a large and growing customer base across active retail and institutional markets. The company has scaled substantially over the last several years and is now investing in the data foundation that will carry the next stage of that growth — the platform underneath its analytics, customer-facing product tooling, and machine learning work.
This is a build role on a team that is actively modernizing rather than maintaining. The data platform is being designed and operationalized now, which means the person in this seat will influence architecture decisions instead of inheriting them.
What You'll Do
As a Senior Data Engineer on the Data & Analytics team, you will stay a step ahead of the people who depend on your work — providing the platform, tooling, and infrastructure that let analytics, product, and ML engineers ship solutions to real business problems. The scope goes well past pipeline maintenance: you will own data architecture, scalable workflows, and real-time solutions that support trading tools, product development, and machine learning.
In This Role You Will
- Design, build, and maintain resilient data pipelines and data lake architecture across both batch and real-time streaming use cases, including high-volume, low-latency processing
- Strengthen observability, alerting, and SLOs across data systems so pipelines are easier to monitor and problems surface early
- Optimize ETL/ELT workflows for performance, scalability, and fault tolerance
- Build dbt workflows supporting partner onboarding and recurring end-of-day reporting
- Build and support event-driven architectures and reusable platform components
- Advance the orchestration and automation of workflows across the platform
- Integrate complex financial APIs and third-party data sources into internal systems
- Partner with analytics, product, and ML engineers to develop and deploy reliable data products
- Build feature pipelines and model-ready datasets in support of machine learning work
- Hold a high bar on code quality, testing, and platform reliability
- Participate in Agile ceremonies and contribute to a collaborative, growth-oriented team culture
What You'll Need
- Bachelor's degree in Computer Science, Engineering, or a related field
- 5+ years of experience in data engineering, platform engineering, or backend development
- Strong SQL and Python skills for building and testing data solutions
- Hands-on experience with GCP and its data products (BigQuery, Cloud SQL, Cloud Storage)
- Experience with AWS services (S3, Glue, Athena, Kinesis) alongside GCP; EMR is a plus
- Experience with CI/CD pipelines, infrastructure-as-code, and version-controlled deployment workflows (Terraform, GitOps)
- Hands-on dbt experience building and maintaining projects — models, tests, macros, documentation, and CI
- Proficiency with workflow orchestration tooling (Airflow, Prefect, or equivalent)
- Working knowledge of data lake architecture, including file formats (Parquet, Avro) and open table formats (Apache Iceberg)
- Familiarity with event-driven and service-oriented architecture
- A track record of building automated, well-tested, observable data systems
- Comfort working independently and collaboratively in a fast-paced Agile environment
Bonus Points For
- Hands-on Kubernetes experience, particularly with data workloads and containerized pipelines
- Streaming technologies (Kafka, Spark Streaming, Flink) and comfort with high-volume, low-latency data flows
- Change data capture tooling (Debezium, Kafka Connect) and real-time integration patterns
- BI tooling such as Looker Studio or QuickSight
- Observability and monitoring tooling (Datadog, Grafana, Prometheus)
- Background in fintech, trading, or derivatives