Senior Staff Software Engineer, Data

Juniper Square

United States

On-site

USD 210,000 - 260,000

Full time

14 days+

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Benefits offered by this job

Health, dental, and vision care

Life insurance

Mental wellness coverage

Flex Time Off

Annual professional development stipend

Job summary

A financial technology firm in the U.S. is looking for a Data Engineering Architect to transform their data operations into a modern, scalable data platform. The ideal candidate will have over 10 years of experience in data engineering and a strong technical background in architecture and analytics. Responsibilities include defining data architecture strategy, leading hands-on engineering efforts, and collaborating with various teams to drive a data-driven culture. The role offers a competitive salary and generous benefits.

Qualifications

10+ years in data engineering or analytics roles.
Proven experience with large-scale data systems.
Strong hands-on experience with modern data stacks.

Responsibilities

Lead the transformation of data engineering and analytics.
Design scalable batch, streaming, and real-time data systems.
Ensure high-quality analytics and actionable insights.

Skills

Data modeling

SQL

Python

Cloud data services

Data governance

Education

Advanced degree in Computer Science, Engineering, or related field

Tools

AWS

Looker

Tableau

Power BI

Spark

Job description

About Juniper Square

Our mission is to unlock the full potential of private markets. Privately owned assets like commercial real estate, private equity, and venture capital make up half of our financial ecosystem yet remain inaccessible to most people. We are digitizing these markets, and as a result, bringing efficiency, transparency, and access to one of the most productive corners of our financial ecosystem. If you care about making the world a better place by making markets work better through technology – all while contributing as a member of a values-driven organization – we want to hear from you.

Juniper Square offers employees a variety of ways to work, ranging from a fully remote experience to working full-time in one of our physical offices. We invest heavily in digital-first operations, allowing our teams to collaborate effectively across 27 U.S. states, 2 Canadian Provinces, India, Luxembourg, and England. We also have physical offices in San Francisco, New York City, Mumbai and Bangalore for employees who prefer to work in an office some or all of the time.

About your role

We are seeking a Data Engineering Architect to lead the transformation of our current data engineering and analytics function into a modern, scalable, product-oriented Data Platform organization. You will define the vision, architecture, operating model, and execution roadmap required to evolve from project-based data delivery to a platform that enables self-service, reliable, governed, and analytics-ready data across the company.

This is a deeply hands-on leadership role for a technical expert who actively designs systems, prototypes solutions, reviews code, and guides teams through complex challenges. You will modernize our data stack, establish platform standards, introduce best practices for reliability and governance, and enable teams across the business to build data products efficiently and safely.

In addition to platform transformation, you will ensure the data ecosystem delivers high-quality analytics and actionable insights. You will define architecture across ingestion, processing, modeling, semantic layers, analytics, and AI/ML enablement, ensuring data is trustworthy, accessible, secure, and performant.

You will work closely with engineering leadership, product teams, analytics, and executive stakeholders to align technology strategy with business outcomes, mentor engineers, and build a data-driven culture. Success in this role means not only delivering a modern platform, but also elevating the team’s capabilities, processes, and ways of working to operate as a true Data Platform organization.

What you’ll do

Architecture & Technical Leadership
- Define and own the end-to-end data and analytics architecture strategy
- Design scalable batch, streaming, and real-time data systems
- Establish standards for data modeling, semantic layers, and reporting
- Lead architecture reviews and technical decision-making
- Drive adoption of modern architectures (lakehouse, data mesh, real-time analytics)
Hands-On Engineering
- Design and prototype critical data platform components
- Write production-quality code for complex or high-impact areas
- Review schemas, transformations, dashboards, and analytics models
- Troubleshoot performance and reliability issues across pipelines and queries
- Optimize workloads for latency, concurrency, and cost
Data Platform & Pipeline Ownership
- Design and architect a scalable data platform supporting ingestion, transformation, and delivery of both structured and unstructured data across batch and real-time pipelines.
- Design a "Data for Agents" strategy, ensuring our data warehouse is structured with the semantic layers and metadata necessary for LLMs to navigate it accurately.
- Build AI-ready data infrastructure, including vector stores, embedding pipelines, and retrieval systems that power LLM and agentic workflows.
- Develop a RAG-ready data architecture that enables trusted enterprise data retrieval with strong lineage, governance, security, and observability.
- Create curated data products and reusable APIs that make high-quality datasets easily consumable by applications, analytics platforms, and AI agents.
- Enable self-service data access for engineering, analytics, and business teams through standardized models, semantic layers, and platform capabilities.
- Partner with AI, product, and engineering teams to support training datasets, feature stores, and production AI inference pipelines.
- Build agentic ETL/ELT pipelines that use AI agents to autonomously discover sources and generate transformations.
- Ensure reliability, scalability, and resilience of the platform, including high availability, monitoring, and disaster recovery readiness.
Analytics & Business Intelligence
- Partner with product, finance, business operations, and leadership teams to define analytics needs
- Design scalable data models for reporting and advanced analytics
- Ensure analytics solutions are performant, trustworthy, and easy to use
- Drive adoption of data-driven culture through reliable insights
Governance, Quality & Security
- Define data governance, lineage, cataloging, and metadata standards
- Establish data quality frameworks and validation processes
- Ensure privacy, compliance, and secure access to sensitive data
- Implement role-based access controls and auditability
Leadership & Collaboration
- Mentor senior engineers, analytics engineers, and data scientists
- Partner with product, ML, platform, and business teams
- Translate business questions into scalable data solutions
- Influence roadmaps using data platform and analytics considerations
- Act as the executive technical authority for data and analytics
Operational Excellence
- Define SLAs/SLOs for data availability, freshness, and accuracy
- Establish monitoring, alerting, and incident response processes
- Optimize cloud costs and query performance
- Support capacity planning for data growth
Culture & Enablement
- Be an evangelist for pragmatic AI adoption.
- Help establish a culture of outcome-driven innovation.

Required Qualifications

Advanced degree in Computer Science, Engineering, or related field
10+ years in data engineering, analytics engineering, or data platform roles
Proven experience architecting large-scale data and analytics systems
Strong hands-on experience with modern data stacks in cloud environments
Deep expertise in data modeling for analytics (dimensional, star/snowflake, Data Vault, etc.)
Advanced SQL skills and proficiency in Python, Scala, or Java
Advanced expertise in dimensional data modeling and semantic layers (e.g., dbt, Cube) to provide "agent-readable" context.
Experience with distributed processing frameworks (Spark, Flink, etc.)
Experience building reporting and BI solutions at scale
Strong understanding of both batch and real-time architectures
Hands-on experience with AWS, Azure, or GCP data services
Experience with BI tools (e.g., Looker, Tableau, Power BI, etc.)
Strong understanding of data governance and security best practices
Ability to operate at both executive and deeply technical levels

Nice to Have

Experience supporting AI/ML pipelines and feature engineering
Familiarity with real-time analytics and event-driven architectures
Experience implementing semantic layers or metrics stores
Background in high-growth SaaS or data-intensive organizations
Experience with experimentation platforms or product analytics

Compensation

Compensation for this position includes a base salary, equity and a variety of benefits. The U.S. base salary range for this role is 210,000 - 260,000 USD and the Canadian base salary range for this role is 220,000 to 270,000 CAD. Actual base salaries will be based on candidate-specific factors, including experience, skillset, and location, and local minimum pay requirements as applicable.

Benefits include: