The Role
Build and operate batch and real-time data pipelines and production data products for an enterprise customer. Integrate databases, APIs, files, and event streams while ensuring data quality, observability, lineage, and reliability. Act as the primary technical customer partner, translating needs into data models, APIs, and delivery plans. Coordinate releases, manage technical projects, resolve production issues, document operations, and provide field feedback to improve the platform.
Summary Generated by Built In
Forward Deployed Data Engineer
About the Role
What You Will Own
Data Engineering
Customer Partnership
Delivery and Project Management
Product Feedback Loop
What Success Looks Like
Required Qualifications
Preferred Qualifications
How You Will Work
About DataSQRL
Equal Opportunity
DataSQRL is hiring a Forward Deployed Data Engineer to embed with one of our enterprise customers and own the success of their data implementation end to end.
This role combines three jobs in one. You are a hands-on data engineer who builds and operates production pipelines and data products. You are the customer's trusted technical partner, working directly with their engineers and business stakeholders. And you are the delivery owner who turns ambiguous goals into scoped plans, drives them to production, and keeps commitments on track.
You will be dedicated to a single customer while remaining part of DataSQRL's engineering organization. What you learn in the field shapes our product: you bring real-world requirements back to the platform team and turn one-off solutions into reusable capabilities.
Data Engineering
- Design, build, test, deploy, and operate batch and real-time data pipelines.
- Integrate data from the customer's source systems via databases, APIs, files, and event streams.
- Build data products with DataSQRL and its underlying technologies, including SQL, Kafka, Flink, PostgreSQL, GraphQL, and cloud infrastructure.
- Implement automated controls for completeness, accuracy, timeliness, lineage, schema changes, and reconciliation.
- Monitor production workloads and diagnose data, pipeline, performance, and integration failures through resolution.
Customer Partnership
- Serve as the customer's primary technical point of contact for their DataSQRL implementation.
- Work with technical and business stakeholders to uncover the real problem behind a request and translate it into data contracts, models, APIs, and acceptance criteria.
- Communicate trade-offs clearly, set realistic expectations, and build confidence in the data and in DataSQRL as a partner.
- Validate delivered capabilities with users to confirm they solve the intended problem.
Delivery and Project Management
- Scope, plan, and prioritize work with customer stakeholders, and maintain a clear roadmap of commitments.
- Coordinate releases, dependencies, and change management across customer and DataSQRL teams.
- Track progress, surface risks early, and escalate decisions that require customer or internal alignment.
- Document data contracts, schemas, transformation logic, runbooks, and known limitations so both teams have full operational visibility.
Product Feedback Loop
- Identify gaps, friction, and recurring patterns in customer deployments.
- Partner with DataSQRL product and engineering to turn field learnings into platform improvements without disrupting customer delivery.
What Success Looks Like
First 90 days: You understand the customer's priority use cases, data sources, stakeholders, environments, and delivery process. You have shipped production-quality pipelines and established a working delivery rhythm and operating documentation.
Ongoing:
- Reliable, accurate, and timely data products.
- Predictable delivery against jointly agreed priorities.
- Fast diagnosis and resolution of production issues.
- Less manual data preparation and fewer recurring data-quality failures.
- Strong stakeholder trust and a customer that sees DataSQRL as a strategic partner.
- Field insights that measurably improve the DataSQRL platform.
Required Qualifications
- 4+ years of experience in data engineering, analytics engineering, backend engineering, or a closely related role.
- Advanced SQL and experience designing relational data models and production transformations.
- Strong proficiency in Python or Java for data processing, integration, testing, and automation.
- Experience building and operating production data pipelines across databases, APIs, files, and cloud services, preferably on AWS.
- Working knowledge of data quality, observability, lineage, schema evolution, reconciliation, and incident response.
- Ability to diagnose problems across source systems, transformation logic, infrastructure, and downstream consumers.
- Excellent written and verbal communication, including with non-technical stakeholders.
- Demonstrated ownership: you can take a requirement from discovery through production operation with limited supervision.
- Experience planning and driving technical projects, including managing scope, timelines, and stakeholder expectations.
Preferred Qualifications
- Experience with Apache Kafka, Apache Flink, PostgreSQL, GraphQL, or comparable technologies.
- Prior experience as a forward deployed engineer, solutions engineer, consultant, or in another customer-embedded or professional services role.
- Experience with CI/CD, infrastructure as code, containerized workloads, and production monitoring.
- Familiarity with data governance, role-based access control, privacy requirements, and secure handling of customer data.
- Startup experience balancing delivery speed, reliability, and evolving requirements.
How You Will Work
DataSQRL employs and manages this position, with your delivery capacity dedicated to your assigned customer. Day-to-day priorities are set with designated customer stakeholders and governed through DataSQRL's delivery and engineering processes.
Because you are embedded with a customer, responsiveness, reliability, and relationship management are core to the job, not secondary responsibilities. You will need sound judgment about when to resolve an issue yourself, when to bring in DataSQRL platform engineering, and when a requirement or trade-off needs customer alignment.
DataSQRL automates the creation of production-grade data infrastructure for operational applications, analytics, and AI. Our platform helps organizations turn fragmented source data into governed, reliable data products and APIs faster, using an open-source foundation and modern streaming and database technologies.
DataSQRL is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, or any other characteristic protected by applicable law.
Skills Required
- 4+ years of experience in data engineering, analytics engineering, backend engineering, or a closely related role
- Advanced SQL and experience designing relational data models and production transformations
- Strong proficiency in Python or Java for data processing, integration, testing, and automation
- Experience building and operating production data pipelines across databases, APIs, files, and cloud services, preferably on AWS
- Working knowledge of data quality, observability, lineage, schema evolution, reconciliation, and incident response
- Ability to diagnose problems across source systems, transformation logic, infrastructure, and downstream consumers
- Excellent written and verbal communication, including with non-technical stakeholders
- Demonstrated ownership from discovery through production operation with limited supervision
- Experience planning and driving technical projects, including managing scope, timelines, and stakeholder expectations
- Experience with Apache Kafka, Apache Flink, PostgreSQL, GraphQL, or comparable technologies
- Prior experience as a forward deployed engineer, solutions engineer, consultant, or in another customer-embedded or professional services role
- Experience with CI/CD, infrastructure as code, containerized workloads, and production monitoring
- Familiarity with data governance, role-based access control, privacy requirements, and secure handling of customer data
- Startup experience balancing delivery speed, reliability, and evolving requirements
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The Company
What We Do
DataSQRL is an open-source data engineering harness that helps AI coding agents build reliable, production-grade data pipelines, APIs, and data products. It provides guardrails, validation, simulation, and feedback to address production requirements for data quality, scalability, governance, and reliability. Using SQL-based definitions, it optimizes processing across engines including Flink, Kafka, Postgres, and Iceberg, and generates validated REST, GraphQL, and MCP endpoints.
