Senior Data Engineer (R14287)

Who We Are Oportun (Nasdaq: OPRT) is a mission-driven financial services company that puts its members' financial goals within reach.

With intelligent borrowing, savings, and budgeting capabilities, Oportun empowers members with the confidence to build a better financial future.

Since inception, Oportun has provided more than $22.7 billion in responsible and affordable credit, saved its members more than $2.5 billion in interest and fees, and helped its members set aside an average of more than $1,800 annually.

Location Note We’re excited to consider talent currently based in India, as well as candidates who are open to relocating to India for this opportunity.

Please note that, as this is a "Remote–India" position, the role must be performed from within India.

The Opportunity As a Senior Data Engineer on the Member Communication Platform team, you'll design and operate secure, scalable data products that power audience selection, eligibility, preferences, consent, campaign inputs, delivery data, and operational reporting.

In your first year, success means delivering reliable Databricks-based pipelines and integrations, improving data quality and observability, and providing technical leadership across cross-functional initiatives that support timely, relevant, and compliant member communications.

At Oportun, we move fast and we hold each other to a high bar.

If you care about doing your best work while actually moving the needle for real people, you'll fit right in.

What You'll Do Your day-to-day You'll lead the design and implementation of scalable, secure data architectures for member communications, defining data models, contracts, lineage, and ownership.

You'll independently lead complex data engineering initiatives from technical requirements and design through production delivery, coordinating work across multiple engineers and stakeholders while proactively managing dependencies, risks, and technical trade-offs You'll design, develop, and optimize production-grade data pipelines and integrations using Databricks, Apache Spark, PySpark, Spark SQL, Delta Lake, and SQL, while modernizing appropriate legacy workloads.

You'll build reliable data-loading, reconciliation, retry, idempotency, recovery, and deployment patterns, and help operate data lakehouse and operational data assets for performance, integrity, availability, and security.

You'll establish data-quality and governance practices, including validation, monitoring, alerts, dashboards, runbooks, and lineage, and partner with Privacy, Compliance, Risk, and Security on PII, preferences, consent, suppression, retention, and access controls.

You'll provide technical leadership through design and code reviews, mentor junior engineers, lead complex production investigations, identify root causes, and implement durable fixes that reduce recurring operational burden.

You'll partner with Product, Communications, Analytics, Engineering, Risk, Privacy, Compliance, and other stakeholders to translate needs and technical trade-offs into actionable solutions, drive release readiness through automated testing, CI/CD, rollout planning, and post-release measurement, monitor pipeline performance, quality, timeliness, cost, and reliability, and look for practical ways to use AI tools thoughtfully to work smarter, reduce manual work, and make better decisions faster.

Who You Are The right fit looks like this You're a hands-on data engineer who enjoys solving complex problems and turning ambiguous business needs into reliable, scalable technical solutions.

You take ownership of production outcomes, communicate clearly across technical and non-technical audiences, and help raise the bar through mentorship, thoughtful design reviews, and continuous improvement.

You're comfortable balancing member experience, privacy, compliance, reliability, performance, and cost when making technical decisions.

What You Bring Skills & experience Required You'll have 6+ years of experience in data engineering, with a focus on data architecture, data pipelines, data platforms, and database or lakehouse management.

You'll have a bachelor's or master's degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience; strong proficiency in Python/PySpark and SQL; and strong leadership, problem-solving, decision-making, and communication skills.

You'll bring strong hands-on experience with Databricks, Apache Spark, Spark SQL, Delta Lake, workflow orchestration, CI/CD, Git-based workflows, automated testing, and end-to-end data integrations or products in production environments.

You'll have experience with data quality, observability, alerting, lineage, cloud platforms and services (especially AWS), secure access controls, secrets management, encryption, auditability, sensitive member data, and Agile ways of working.

Nice to Have Experience in financial services, lending, marketing technology, customer communications, or another regulated environment.

Familiarity with Unity Catalog or comparable data-governance tooling, and experience with Java or Scala.

You've used AI tools such as ChatGPT, Copilot, or similar tools to improve engineering productivity, testing, documentation, troubleshooting, data quality, or operational effectiveness.

Familiarity with using AI agents or automations to research, prototype, test, document, and deliver solutions across a broad technology stack with sound judgment for security, privacy, accuracy, and human review.

Our AI Mindset We build with AI.

We lead with judgment.

AI isn't a feature here.

It's how we work.

We're excited about what AI makes possible, and we're looking for people who share that curiosity.

You don't need to be an expert.

You need to be open to experimenting , willing to learn, and able to apply new tools in ways that actually move the needle.

In this role, you'll be encouraged to explore AI tools, find practical use cases for your function, and bring ideas for working sm