Software Engineer - Data Platform
Job description
Software Engineer - Data Platform at Basic Capital.
About the role
Operational data becomes business intelligence, compliance reporting, and customer communications through this role. The position establishes data infrastructure as a founding member of a growing team.
Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.
Key facts
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Location: USA
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Engagement: Full-time
- Degree: Required
- Visa: Not listed
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Compensation: Not listed
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Team: Not listed
- Years: Not listed
What you'll do
Raw operational events are shaped into business intelligence, compliance reporting, and customer communications by this role. Automated workflows owned here execute critical compliance reporting, notices, and regulatory submissions. Active participation in design discussions and code reviews across the stack guides architecture decisions and implementation details.
Requirements
Strong experience with ingestion, transformation, and reliability patterns underpins data pipeline development and ETL/ELT workflows. Accurate, performant schemas for analytics and reporting emerge from SQL proficiency and data model design. Consistent outputs depend on data transformation tools like dbt working with modern data warehouse platforms such as Snowflake, BigQuery, and Redshift. Stakeholder needs are served by dashboards built and maintained with data visualization tools including Looker, Tableau, Hex, or Metabase. Correctness in compliance-critical use cases demands detail orientation and commitment to data accuracy. Production systems require proficiency in at least one of these languages: Kotlin, Rust, Java, Go, C/C++, Python, Ruby, or TypeScript. Deployment and monitoring operate on modern build systems and cloud infrastructure across AWS, GCP, or Azure.
Nice to have
Context for scaling financial products appears with experience in fintech or early-stage technology companies. Clearer data definitions and metrics arise from understanding financial data, ledgers, and accounting principles. Performance and reliability in transaction-heavy flows are supported by Kotlin backend experience. Complex, long-running processes across services are managed through asynchronous workflow orchestration using Temporal.
Practical notes
The role is based in the New York Office and requires an in-office presence. You must hold a degree to be considered for this position. Typical interview steps
Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.
Good to know
Data platform engineers turn operational data into decisions using pipelines, warehouses, and analytics tools. Fintech environments often blend compliance reporting with customer-facing insights that must meet regulatory standards. Modern stacks commonly combine cloud providers, data transformation frameworks, and visualization tools integrated through APIs. Collaboration across product, compliance, and engineering is typical in product-driven data teams that own their full lifecycle. Solving large-scale social problems intersects with pragmatic, technology-driven solutions that aim to expand access and transparency.
Career growth
Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.