Senior Staff Software Engineer – Data Platform

Remote from
USA flag
USA
Salary, yearly, USD
200,500 - 250,600
Employment type
Full Time,
Job posted
Apply before
2 Jul 2026
Experience level
Senior
Views / Applies
18 / 7

About Marqeta

The global modern card issuing platform.

Actively Hiring
Verified job posting
This job post has been manually reviewed for authenticity and compliance.

AI Summary

This Senior Staff Software Engineer role at Marqeta focuses on building and operating the data platform, including lakehouse infrastructure, streaming ingestion, and abstractions for data and ML teams. The position requires deep expertise in modern lakehouse technologies like Iceberg/Delta on S3, Spark, Kafka, and Python, with a track record of evolving live infrastructure. The role is hands-on, involving production code, design reviews, and setting technical direction. The team follows a flexible-first policy with remote work within the US or Oakland office, and participation in on-call rotation is required.

Role DNA

Job Complexity
Easy Hard
Pace & Pressure
Relaxed Fast-paced
Autonomy Level
Guided Full Ownership
Communication Load
Independent Highly Collaborative
AI Insight This role demands extensive experience (around a decade), deep technical expertise in data platforms, and ownership of complex infrastructure transitions, making it highly challenging.

Salary Analysis

Median Highly Competitive
USD225,550
US Market
USD150k – USD300k
0 USD330k
AI Insight The offered salary range of $200,500 to $250,600 is competitive for a senior staff data platform role, aligning with the 75th to 90th percentile of the US market. The median of $225,550 is above the market median for similar roles, reflecting the high seniority and specialized skills required.

Key Skills

Data Platform Lakehouse Apache Iceberg Apache Spark Kafka Python AWS Infrastructure Streaming Software Engineering

Dear Hiring Manager,

I am excited to apply for the Senior Staff Software Engineer - Data Platform role at Marqeta. With over a decade of experience building data platforms and infrastructure, I have a strong track record of designing and shipping production-grade lakehouse solutions using Iceberg, Spark, and Kafka. I thrive on creating abstractions that enable teams to work efficiently and reliably.

In my previous role, I led the migration of a legacy data warehouse to a modern lakehouse architecture, improving query performance by 40% and reducing operational overhead. I am particularly drawn to Marqeta's focus on engineering excellence and the opportunity to shape the multi-year technical direction of the data platform.

I am eager to bring my expertise in Python, Go, and AWS data services to your team and contribute to building the paved road that internal teams rely on. Thank you for considering my application.

Sincerely, [Your Name]

Can you describe a time you designed and implemented a data platform abstraction that was adopted by multiple teams?
At my previous company, I designed a unified ingestion layer using Kafka and Spark that allowed teams to land data with a simple API, handling schema evolution and partitioning automatically. This abstraction reduced time-to-production for new data sources by 60% and was adopted by five teams within the first quarter.
How would you approach migrating a production data platform from a legacy system to a modern lakehouse architecture with minimal downtime?
I would start by assessing the current system's data flows and dependencies, then design a dual-run strategy where both systems operate in parallel. Using change data capture and incremental processing, we can gradually shift traffic to the new lakehouse while monitoring for consistency. Key steps include thorough testing, rollback plans, and communication with stakeholders to schedule migrations during low-traffic periods.
Explain the trade-offs between Apache Iceberg and Delta Lake for a multi-tenant data platform. Which would you choose and why?
Both Iceberg and Delta provide ACID transactions and time travel, but Iceberg offers better integration with Spark and a more open ecosystem, while Delta has tighter integration with Databricks. For a multi-tenant platform on AWS S3, I would lean towards Iceberg due to its independence from proprietary engines and strong support for partitioning and schema evolution. However, if the team already uses Databricks heavily, Delta might be more convenient.
Describe a complex performance issue you resolved in a production data system. What was the root cause and how did you fix it?
We had a Spark job that processed streaming data and was slowing down over time due to small file accumulation in S3. The root cause was that the job was writing too many small parquet files, causing metadata overhead. I redesigned the compaction strategy to coalesce files into larger batches and implemented a periodic compaction job using Iceberg's rewriteDataFiles action, which improved query performance by 50%.
How do you ensure reliability and developer experience when building a data platform used by multiple teams?
I prioritize clear SLAs, monitoring, and self-service tooling. For reliability, we implement automated testing, canary deployments, and on-call rotations with thorough runbooks. For developer experience, we provide well-documented APIs, SDKs, and examples, and we regularly collect feedback from users to iterate on the platform. Blameless postmortems help us learn from incidents without fear.

This is a software engineering role on a software engineering team. The team builds and operates the data platform: the infrastructure, abstractions, and SDKs that the rest of Marqeta’s data and ML organization runs on. We are not writing pipelines–we are building the platform pipelines run on.

If you’ve spent your career building infrastructure that other engineers depend on; and you know the difference between “software engineering” and “data engineering”, read on.

We work Flexible First. This role can be performed remotely anywhere within the United States or from our Oakland office. We’d love for you to join us!

Learn more about our Product and Engineering team.

At Marqeta, participation in a rotational on-call pager duty is a required part of the software engineering role. The specifics of the rotation may vary by team, depending on team size and structure, and will be discussed further during the interview process.

What you’d own

The lakehouse and the streaming ingestion that feeds it. Iceberg/Delta on S3, Spark/Glue processing, Kafka and CDC pipelines, the abstractions that let other teams land data, build pipelines, and publish datasets without inventing the patterns themselves. You’d help set the multi-year technical direction, design the golden paths, and ship the work — directly, with your hands on the code that matters.

How the team works

You’d report to the Director of Data Engineering and partner with a peer Manager whose scope is people leadership and operational delivery. You own technical direction, application architecture, and the engineering bar. You’ll work alongside other senior staff engineers across the broader data platform org, and partner with security, compliance, and the consumer teams who depend on what we ship.

What “hands-on” actually means here

Worth being specific, because “hands-on” means different things to different people:

  • Yes: writing production code on the platform’s hardest problems, owning the design and build of new abstractions end-to-end, leading design reviews, setting the engineering bar through the work itself.
  • Sometimes: prototyping, deep diving on a tricky migration, pairing with engineers on tough problems.

What the work looks like

  • Modernization. Building out the lakehouse — the patterns Marqeta will run on for years. New abstractions, new tooling, the paved road internal teams want to use.
  • Live system work. Evolving and hardening the platform that’s already in production. Reliability, performance, developer experience — improved without disrupting the teams who depend on it.
  • Engineering excellence. Testing confidence, release velocity. On call, blameless postmortems.

What you bring

We care more about demonstrated work than years on paper, but the experience this role asks for typically takes around a decade to accumulate.

  • A career building data platforms, not just pipelines. You can name the abstractions you’ve designed, the teams that adopted them, and the problems they solved. If most of your last several years has been writing transformations rather than building the system transformations run on, this isn’t the right shape.
  • Production depth in modern lakehouse work. Iceberg or Delta in production. Strong fluency in the surrounding stack: AWS data services (S3, Glue, EMR), Spark, Airflow, Kafka, IAM boundaries for multi-tenant data access. You don’t need depth in all of it — you need real depth in the lakehouse and credible engagement with the rest.
  • Mastery of Python in a production data-platform context, with credible depth in Go or Java. Idioms, ecosystem, performance characteristics, the parts that bite at scale. The ability to adopt the best tool for the job.
  • A track record of evolving live infrastructure. Improving reliability, performance, and DX on systems that real teams depend on, sequenced so the work doesn’t disrupt them.
  • Ownership of major platform transitions. Build vs. buy, migration design, risk management, all the way through to delivery. You’ve led at least one of these end-to-end and can talk through what went right, what didn’t, and what you’d do differently.
  • Influence as a working skill. You build alignment across platform, security, and consumer teams. You write design docs and RFCs people actually read. You can hold a strong technical position and update on evidence.
  • A history of growing other engineers — including peers at your level. The team gets stronger because you’re on it.

Helpful, not required: payments or fintech context (PCI-DSS, SOX, SOC 2). CDC tooling experience (Debezium, Kafka Connect). Data governance tooling (DataHub, Amundsen, Collibra). Public technical writing or open-source contributions to data infrastructure.

Typical Process

  • Application Submission
  • Recruiter Video Call
  • Coding Interview & Hiring Manager Interview
  • Virtual “Onsite” consisting of four 45-60 min video calls
  • Offer!

Compensation and Benefits

Marqeta is a Flex First company which allows you to choose your best working environment, whether that be from home or at a company office. To support Flex First, we calibrate pay to a competitive value according to working location. Compensation is aligned according to three tiers within the United States:

  • National: A baseline tier that applies to most of the geographic territory of the United States.
  • Premium: Slightly elevated from the National tier, and oriented toward a narrower set of higher cost-of-living areas, such as Los Angeles CA and Seattle WA
  • Premium Plus: A tier for the most expensive working areas, like the San Francisco Bay area and New York City.

Visit this page or consult with a Recruiter to determine which tier would be applicable to you.

When determining salaries, we consider several factors including, but not limited to, skills, prior experience, and work location. The new-hire base salary range for this position is:

  • National: 200,500 – 250,600
  • Premium: 210,200 – 262,800
  • Premium Plus: 220,000 – 275,000

We also believe in recognizing the contributions of our people. That’s why we award annual bonuses to eligible employees, rewarding both individual performance and the success of the entire company.

Along with monetary compensation, Marqeta offers

  • Multiple health insurance options
  • Flexible time off – take what you need
  • Retirement savings program with company contribution and after tax contributions
  • Equity in a publicly-traded company and an Employee Stock Purchase Program
  • Family-forming benefits, fertility support, and up to 20 weeks of Parental Leave
  • Free therapy sessions, financial and professional coaching, and legal advice
  • Monthly stipend to support our remote work model
  • Annual “development dollars” to support our people growth and development
  • Through Flex First, the freedom to live and work wherever you and your family thrive

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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