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Tech Stack
Tools & technologiesCloudElasticSearchETLKafkaPulsarPythonSparkSQL
About the role
Key responsibilities & impact- Lead one of Grata's Data Product Pillars and own delivery and people leadership
- Ensure successful data and product deliveries, including data coverage, freshness, quality, system performance, and commercial impact
- Coach engineers on growth plans and promotions
- Establish and evolve team practices for planning, execution, quality, data validation, and operational excellence
- Represent Engineering in cross-functional planning with Product, Data, AI, Design, Data Operations, and customer-facing partners
- Manage data platform debt, schema evolution, pipeline reliability, and operational risk
- Translate product, data, and technical vision into execution plans, task breakdowns, and measurable outcomes
- Guide architecture, unblock teams, and support high-impact data initiatives
- Build and scale Grata's proprietary company data platform, including ingestion, enrichment, normalization, entity resolution, quality controls, and serving layers
- Improve freshness, coverage, and accuracy of company profiles, financials, transactions, ownership, firmographics, and market intelligence
- Automate data workflows and turn messy sources into trusted product experiences
- Develop platform capabilities that make Grata's data more searchable, actionable, and defensible across M&A business development workflows
- Strengthen observability, evaluation, and incident response for data systems
Requirements
What you’ll need- Experience leading engineering teams that build data-intensive products, proprietary datasets, or data platform capabilities at scale
- Strong comfort with Python, SQL, and backend services in a data-heavy environment
- Ability to guide full-stack/API work when data capabilities surface in the product
- Experience designing and operating ETL/ELT pipelines, data quality systems, and queue-based, event-driven, or asynchronous workflows
- Strong grasp of data modeling, entity resolution, schema evolution, reliability, observability, architectural patterns, and engineering trade-offs
- Proficiency with AI-driven and agentic coding workflows
- Ability to define acceptance criteria, evaluations, tests, metrics, and review practices
- Ability to connect technical and data investments to customer value, product strategy, and commercial outcomes
- Nice to have: familiarity with Databricks, Spark, distributed data processing, Kafka, Pulsar, Elasticsearch/OpenSearch, graph databases, search/indexing infrastructure, LLM-assisted data workflows, entity resolution, deduplication, taxonomy/ontology design, knowledge graph-style data models, cloud-native architectures, and scalable backend services
- Strong judgment, ownership, and collaboration skills
- Ability to anticipate cross-team constraints, balance technical and data risk, forecast capacity, negotiate scope, plan across quarters, communicate with technical and non-technical partners, guide architectural evolution, and improve processes and data quality feedback loops
Benefits
Comp & perks- Health insurance (medical, dental, vision)
- Retirement savings plan
- Paid time off
- Potential eligibility for bonuses, commissions, or overtime if applicable