Senior Data Platform Engineer
Build and operate reusable data platform services, connectors, and reliable distributed workloads that make enterprise data usable for applications, analytics, and AI. Requires 4+ years of data platform experience plus strong Python, SQL, backend, and data infrastructure skills.
About the job
Responsibilities
- Build core data platform capabilities, including services and APIs for data access, query execution, ingestion, and synchronization.
- Develop reusable connector frameworks and abstractions for integrating enterprise systems and supporting varied access patterns.
- Improve query and data-movement performance through partitioning, parallelism, and efficient memory and storage management.
- Build orchestration, retries, checkpointing, and observability to ensure reliable workload recovery and data correctness.
- Take platform capabilities from design through production in collaboration with product and engineering teams.
Requirements
- 4+ years of experience as a Data Platform Engineer.
- Strong Python and SQL skills, with experience building maintainable, tested production software.
- Experience developing backend services, data infrastructure, or data platform components beyond configuring pipelines.
- Understanding of relational databases, query optimization, data formats, and the tradeoffs between querying data in place and materializing it elsewhere.
- Experience with concurrent or distributed workloads, failure handling, resource management, and performance debugging.
- Ability to investigate unfamiliar systems and translate complex requirements into practical designs.
Nice to Have
- Experience with analytical query engines, lakehouse technologies, workflow orchestration, enterprise-system integrations, or containerized infrastructure.
- Experience building connector libraries, developer-facing APIs, or shared platforms used by engineering teams.
- Familiarity with cloud infrastructure, including networking, storage, identity and access management, and infrastructure as code.
Compensation and Benefits
- Annual salary range: $200,000–$300,000.
- Build AI products addressing complex enterprise problems at the intersection of AI, ERP systems, and automation.
- High ownership, impact, and influence on product direction.
- Collaboration with a technical and customer-focused team.
Skills
Python, SQL, Backend Services, Data Infrastructure, Relational Databases, Query Optimization, Data Formats, Distributed Systems, Workflow Orchestration, Connector Frameworks, Analytical Query Engines, Lakehouse Technologies, Containerized Infrastructure, Cloud Infrastructure, Infrastructure As Code
Similar jobs
Data Engineering jobs
Decagon
San Francisco, CASenior Data Infrastructure Engineer responsible for building and operating reliable, low-latency streaming and batch data systems that support AI products. Requires 5+ years of production data infrastructure experience and expertise with technologies such as Kafka, Flink, ClickHouse, and Terraform.
Harvey
San Francisco, CABuild and lead the central data platform, covering ingestion, warehousing, orchestration, streaming, self-service frameworks, and trust layers. The role requires 5+ years of production data infrastructure experience, strong Python and SQL skills, and expertise with Snowflake and modern data tooling.
Datadog
Boston, MABuild and operate large-scale revenue data pipelines powering billing and cost attribution, while improving reliability, latency, and correctness. The role requires strong Spark and Airflow experience, cross-functional problem-solving, and operational ownership of mission-critical production systems.
Coinbase
United States
Build and operate low-latency systems that capture, normalize, and distribute real-time market data for institutional trading. The role requires backend engineering experience, Java or C++, market data infrastructure knowledge, and exchange connectivity expertise.
Plaid
New York, NY
Build and own large-scale data models, batch and real-time pipelines, and data infrastructure that provide reliable datasets and insights across Plaid. The role requires 4+ years of data engineering experience, strong SQL and Python skills, and expertise with modern warehouses, lakes, and orchestration tools.