Staff Data Engineer

Dow Jones & Company•New York City, NY

2d•$160,000 - $200,000

About The Position

The Staff Engineer, Snowflake Platform is an individual-contributor leadership role on Dow Jones’ Data Architecture & Platform Engineering team. The team operates the data infrastructure supporting Dow Jones products, analytics, and AI across B2B, B2C, and internal use cases. It owns the Snowflake footprint, the surrounding platforms and patterns, and the data-governance framework, with a focus on scale, reliability, security, and cost efficiency. The role owns the Snowflake platform end to end, including enterprise account architecture and administration, security and access controls, and the DevOps systems that make Snowflake safe and accessible across teams and business units. It also leads the platform’s AI approach, including cost-managed access to Cortex and other AI features, patterns for LLM-assisted workflows, and standards for building AI applications on governed data. This is a hands-on leadership position: the Staff Engineer delivers high-stakes Snowflake work, sets platform standards, leads architecture and design reviews, and mentors engineers. The role works through influence and partners with security, compliance, finance, product, and engineering leaders to build durable data-platform infrastructure. The advertised base pay range is $160,000–$200,000.

Requirements

At least 7 years of experience in data engineering, platform engineering, or infrastructure roles, including experience at large or enterprise-scale companies.
Deep, hands-on experience operating a production Snowflake account at scale, including administration, RBAC, security posture, cost, and performance.
Strong Terraform experience, using the Snowflake provider or an equivalent, and experience with CI/CD for data infrastructure.
A track record designing and operating data platforms serving multiple teams, business units, or downstream systems.
Security fluency, including RBAC design, data masking, access policies, private connectivity, SSO, SCIM, and audit posture.
Fluency with observability, monitoring, incident response, and on-call for data infrastructure.
Strong Python skills for platform tooling, automation, or service work.
Experience in regulated or audited environments, such as SOC 2, SOX, or GDPR environments, or equivalent.
Bachelor’s degree in Computer Science, Engineering, or equivalent experience.
Ability to influence across teams and functions without formal authority, supported by strong writing and speaking skills.
Thought leadership about AI and how to use it effectively at scale on a data platform.

Nice To Haves

Experience with dbt, Airflow, or comparable orchestration and transformation tools.
Snowflake certifications, such as SnowPro Advanced: Administrator or Data Engineer.
Substantial Snowflake cost optimization or FinOps experience.
Experience with data governance, metadata, or lineage tools such as Collibra, Atlan, or Alation.
Cloud platform depth, especially AWS, and experience with platform operations at scale.
Experience with near-real-time or event-driven data processing.
Prior experience developing engineers through mentoring, design reviews, or communities of practice.

Responsibilities

Own the Snowflake footprint across Dow Jones, including accounts, warehouses, roles, databases, and the operating model.
Design and evolve RBAC, column- and row-level policies, masking, tagging, and data classification across business units.
Build and maintain Snowflake DevOps pipelines, including Terraform, CI/CD for schema and object changes, automated testing, and change management.
Set and enforce standards for security, network policies, private connectivity, SSO, SCIM, and secrets management.
Optimize cost and performance through resource monitors, warehouse right-sizing, query analysis, credit budgeting, and FinOps reporting.
Design platform reliability capabilities, including replication, failover, disaster recovery, incident response, and observability.
Explore and implement emerging AI tooling to advance modern data engineering practices.
Lead architecture and design reviews for major Snowflake initiatives.
Anticipate scalability, reliability, security, and cost problems.
Reduce platform technical debt and complexity to help downstream teams work more effectively.
Debug difficult problems, lead during incidents, and identify root causes.
Mentor engineers across levels, including senior and staff peers, through design guidance, code review, pairing, and documentation.
Contribute to engineering development practices, career frameworks, and communities of practice.
Communicate through design documents, wiki content, and technical talks, tailoring communication to engineers, product, security, and executives.