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Staff Data Engineer

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What you’ll work on

Full posting
  • Implement per-user and per-location access in BigQuery with row-access policies and column masking

  • Design purpose-based access control (raw, anonymized, or aggregated) with policies enforced in BigQuery

  • Build an MCP service over the LookML semantic layer where AI agents inherit the user's access

  • Set guardrails on AI and ad-hoc query spend using quotas, bytes-billed limits, and slot reservations

  • Document security patterns and access procedures so the platform is easy to operate and review

From the employer’s posting
Implement per-user and per-location access in BigQuery using row-access policies and column-level masking
Design and build purpose-based access control (raw, anonymized, or aggregated data depending on the declared purpose)
Build an MCP service over our LookML semantic layer.
Put guardrails on AI and ad-hoc query spend using custom quotas, maximum-bytes-billed limits, slot reservations, and caching.
Document security patterns and access procedures so the platform is easy to operate, review, and extend.
Apply at Pearson

Posting record

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Actively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.

Status in our records
Live
First seen by us
Oct 29, 20266 days ago
Recorded sightings
7
Last seen by us
Nov 4, 2026

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Day 6

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Pearson is hiring

11 open jobs

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See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SQL
  • BigQuery
  • Databricks
  • Snowflake
  • Trino
Where the posting names them
+ Strong SQL, plus enough familiarity with a semantic / BI layer (LookML or similar) to expose it safely. + Python or a comparable language for service development. + Comfort working on your own in a greenfield setting and setting patterns others follow.
+ Platform and integration engineering skills: you can build and run a service that handles authentication and passes user identity through to the data (OAuth or similar). + Strong SQL, plus enough familiarity with a semantic / BI layer (LookML or similar) to expose it safely. + Python or a comparable language for service development.
**What you'll do** + Enforce access in the data. Implement per-user and per-location access in BigQuery using row-access policies and column-level masking, so each person sees only the data they're entitled to. + Make purpose part of the policy. Design and build purpose-based access control (raw, anonymized, or aggregated data depending on the declared purpose), with policies authored in Collibra and enforced natively in BigQuery.
+ Enforce access in the data. Implement per-user and per-location access in BigQuery using row-access policies and column-level masking, so each person sees only the data they're entitled to. + Make purpose part of the policy. Design and build purpose-based access control (raw, anonymized, or aggregated data depending on the declared purpose), with policies authored in Collibra and enforced natively in BigQuery. + Open a governed door for AI. Build an MCP service over our LookML semantic layer. AI agents authenticate as the user and inherit that user's access, with no separate back door.
**What you'll bring** + Hands-on experience with fine-grained security on a cloud data warehouse: row-level and column-level security, masking, authorized views, and IAM. BigQuery is ideal, and Snowflake or Databricks experience also counts. + A solid grounding in data security and governance: role- and attribute-based access control, PII handling, and anonymization / masking.
+ MCP or other LLM tool-use / agent integrations; Looker API or embedding. + BigQuery cost governance. + Collibra policy automation.
+ Collibra policy automation. + Federated query engines (Trino / Starburst). + Experience with FERPA, HIPAA, or other regulated data.

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About the role

We file this job under Data Engineer, by the work the posting describes.

Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.

Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.

An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.

US government figures for database architects

Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs

The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.

Job description

View original posting ↗

Job Title: Staff Data Engineer **About the role** Pearson Virtual Schools runs online K-12 programs, including Connections Academy and Pearson Online Academy, for students and families across the country. Our data includes some of the most sensitive information there is: student assessment results and records protected under FERPA. As AI tools start working with that data next to our people, the access rules have to be right for both. We're starting a new Data Engineering group, and this is one of its first two hires. You'll design and build how our data is secured and served to AI from the start. That means fine-grained access enforced in the data itself, plus a governed gateway, built on the Model Context Protocol (MCP), through which AI agents act _as the user_ : same models, same permissions, one security model for people and AI. This is a greenfield, senior technical role. You'll be the go-to expert on data security and AI access, make the key architecture decisions, and mentor the engineers around you. **What you'll do** + Enforce access in the data. Implement per-user and per-location access in BigQuery using row-access policies and column-level masking, so each person sees only the data they're entitled to. + Make purpose part of the policy. Design and build purpose-based access control (raw, anonymized, or aggregated data depending on the declared purpose), with policies authored in Collibra and enforced natively in BigQuery. + Open a governed door for AI. Build an MCP service over our LookML semantic layer. AI agents authenticate as the user and inherit that user's access, with no separate back door. + Keep costs bounded. Put guardrails on AI and ad-hoc query spend using custom quotas, maximum-bytes-billed limits, slot reservations, and caching. + Design end to end. Work closely with our Senior Analytics Engineer (Semantic Layer) so security and the semantic layer fit together cleanly. + Make it auditable. Document security patterns and access procedures so the platform is easy to operate, review, and extend. **What you'll bring** + Hands-on experience with fine-grained security on a cloud data warehouse: row-level and column-level security, masking, authorized views, and IAM. BigQuery is ideal, and Snowflake or Databricks experience also counts. + A solid grounding in data security and governance: role- and attribute-based access control, PII handling, and anonymization / masking. + Platform and integration engineering skills: you can build and run a service that handles authentication and passes user identity through to the data (OAuth or similar). + Strong SQL, plus enough familiarity with a semantic / BI layer (LookML or similar) to expose it safely. + Python or a comparable language for service development. + Comfort working on your own in a greenfield setting and setting patterns others follow. + Usually 8+ years in data engineering, data platform, or data security engineering, including leading at least one critical technical initiative. **Nice to have** + MCP or other LLM tool-use / agent integrations; Looker API or embedding. + BigQuery cost governance. + Collibra policy automation. + Federated query engines (Trino / Starburst). + Experience with FERPA, HIPAA, or other regulated data. _Don't tick every box? Apply anyway. If you've secured sensitive data at scale and want to work out how AI should access it safely, we'd like to hear from you._ **Why you'll love it** + A rare greenfield problem. Secure, identity-aware AI access to governed data is still new ground, and you'll help define it. + Work that protects students. You'll safeguard data that belongs to kids and families. + Senior scope without managing people. You'll own the architecture as an individual contributor. + Shape a new team. As an early hire, your patterns become the group's patterns. \#LI-Hybrid **Who we are:** At Pearson, our purpose is simple: to help people realize the life they imagine through learning. We believe that every learning opportunity is a chance for a personal breakthrough. We are the world's lifelong learning company. For us, learning isn't just what we do. It's who we are. To learn more: We are Pearson. Pearson is an Equal Opportunity Employer and a member of E-Verify. Employment decisions are based on qualifications, merit and business need. Qualified applicants will receive consideration for employment without regard to race, ethnicity, color, religion, sex, sexual orientation, gender identity, gender expression, age, national origin, protected veteran status, disability status or any other group protected by law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act. If you are an individual with a disability and are unable or limited in your ability to use or access our career site as a result of your disability, you may request reasonable accommodations by emailing TalentExperienceGlobalTeam@grp.pearson.com. **Job:** Engineering **Job Family:** TECHNOLOGY **Organization:** Virtual Learning **Schedule:** FULL\_TIME **Workplace Type:** Hybrid **Req ID:** 26355 \#LI-REMOTE
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Virtual, LKA

+ Shape a new team. As an early hire, your patterns become the group's patterns. \#LI-Hybrid **Who we are:**
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**Schedule:** FULL\_TIME **Workplace Type:** Hybrid **Req ID:** 26355 \#LI-REMOTE
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