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Director Data Platform Jobs (NOW HIRING)

Docusign

Sr. Data Platform Engineer

San Francisco, CA · On-site

$134K - $161K/yr

Reporting to the Sr. Director, Data Platform and ML Operations, this role serves as a technical expert on Snowflake and AI infrastructure, with a strong focus on production-grade reliability and ...

DocuSign

Sr. Data Platform Engineer

San Francisco, CA · On-site +1

$134K - $161K/yr

Reporting to the Sr. Director, Data Platform and ML Operations, this role serves as a technical expert on Snowflake and AI infrastructure, with a strong focus on productiongrade reliability and ...

The Subway

Director Data Engineering

Shelton, CT · On-site

Director, Data Engineering - Shelton, CT Ready to build what's next with one of the world's most ... Data Platform Engineering & Architecture * Own the design, development, and operations of Subway ...

Scotiabank

Director, Data Enablement

Dallas, TX · On-site

$180 - $260/hr

Director, Data Enablement Requisition ID: 269936 Salary Range: - Please note that the Salary Range ... Platform Enablement & Operating Model * Lead enablement across data ingestion, transformation ...

Core Specialty

Director, Data Engineering

Dallas, TX

- We are looking for a Director, Data Engineering, to lead the continued optimization and maturation ... A key part of the mandate is ensuring the platform remains AI/ML ready as it scales - well modeled ...

Merck & Co.

Director Data AI & Analytics

Irvine, CA · On-site

$154.57 - $216.10/hr

The Director Data, AI and Analytics will own the strategy, platforms, operating model, and outcomes for Data, AI, and Analytics across three FUJIFILM life-sciences businesses. In highly regulated GxP ...

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Director Data Platform information

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$52K

$128.5K

$200K

How much do director data platform jobs pay per year?

As of Aug 23, 2026, the average yearly pay for director data platform in the United States is $128,526.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,000.00 and $163,500.00 per year, depending on experience, location, and employer.

What does a director data platform do?

A Director of Data Platform is responsible for leading the strategy, architecture, and management of an organization's data infrastructure. They oversee teams that build and maintain data platforms, ensure data quality and security, and enable data-driven decision making across the company. The role requires close collaboration with engineering, analytics, and business teams to support current and future data needs. Additionally, they evaluate new technologies and ensure the data platform scales with business growth.

What are the key skills and qualifications needed to thrive as a director data platform, and why are they important?

To thrive as a Director Data Platform, you need deep expertise in data architecture, data engineering, and analytics, usually backed by a degree in computer science or a related field and significant leadership experience. Familiarity with cloud platforms (like AWS, Azure, or GCP), big data tools (such as Hadoop or Spark), and data governance frameworks is critical, along with certifications in cloud or data management. Exceptional strategic thinking, communication, and stakeholder management help drive cross-functional alignment and innovation. These capabilities ensure robust, scalable data infrastructures that support business goals and enable data-driven decision-making.

What are the main challenges a director data platform faces when scaling data infrastructure for a growing organization?

A Director of Data Platform often faces challenges related to balancing scalability, performance, and cost efficiency as the organization grows. Ensuring data security and compliance across multiple systems, integrating diverse data sources, and maintaining high data quality can be complex, especially as teams and data volumes expand. Additionally, the role requires close collaboration with engineering, analytics, and business teams to align the data platform with evolving business needs and to foster a data-driven culture. Proactive communication, strategic planning, and continuous learning are key to overcoming these hurdles.

What is the difference between Director Data Platform vs Data Engineer?

AspectDirector Data PlatformData Engineer
CredentialsTypically requires advanced degrees (Master's or PhD) in Computer Science, Data Science, or related fields; leadership experienceBachelor's or Master's in Computer Science, Data Engineering, or related fields; certifications like AWS, Google Cloud, or Azure are common
Work EnvironmentLeads teams, oversees data platform architecture, strategic planning, and cross-department collaborationBuilds, develops, and maintains data pipelines, databases, and ETL processes
Industry UsageCommonly found in organizations with large-scale data needs, overseeing data infrastructureHands-on role in data processing, often working closely with data scientists and analysts

The Director Data Platform focuses on strategic leadership, architecture, and team management of data infrastructure, while Data Engineers are more involved in the technical development and maintenance of data pipelines and systems. Both roles are essential but differ in scope and responsibilities.

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Director Data Platform jobs near you
Infographic showing various Director Data Platform job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $128,526 per year, or $61.8 per hour.

Senior Director, Data Platform Engineering

Lila Sciences

San Francisco, CA

$79.25 - $106/hr

Full-time

Re-posted 24 days ago

Job description

Your Impact at LILA

Lila is seeking a highly motivated and experienced engineering leader to lead a team responsible for our Lila's product data platform. You will own the data platform and infrastructure end-to-end - architecture, delivery, reliability, and developer/data scientist experience.

Our mission is to deliver Scientific Super Intelligence through a reliable, scalable, and self-service infrastructure for data ingestion, storage, processing, and interaction - enabling AI/ML, product teams, and scientists to build data-intensive applications with confidence and speed. Our platform supports analytical and machine learning workloads across Lila, serving autonomous DBTL cycles, instrument data pipelines, and AI inference workflows.

You will be responsible for building and leading a team of talented engineers, driving technical strategy, and ensuring the scalability and performance of our data management and data serving capabilities of our Data Platform. You will work closely with data scientists, data engineers, lab scientists, and product teams to understand their needs and deliver innovative solutions that leverage the power of cutting edge data processing technologies.

What You'll Be Building

  • Team Leadership: Build, mentor, and manage a high-performing team of 30-40 data engineering experts. Evaluate and adopt modern data infrastructure - including real-time streaming (Kafka, Flink), columnar engines (DuckDB, ClickHouse), lake house, and cloud-native object storage architectures; Foster a culture of collaboration, innovation, and continuous improvement; Provide technical guidance and mentorship to team members, promoting their professional growth; Conduct performance reviews, provide feedback, and identify opportunities for training and development; Manage team workload, prioritize projects, and ensure timely delivery of high-quality solutions.
  • Technical Strategy and Execution: Define and execute the technical roadmap for our data platform, aligning with Lila's overall data strategy; Drive innovation in data Lakehouse and data serving ecosystem exploring new technologies and approaches to improve usability, performance, scalability, and efficiency; Ensure the reliability, availability, and security of our data processing infrastructure.; Collaborate with other engineering teams to integrate our data processing technologies with other Lila systems and services.
  • Stakeholder Management: Partner with data scientists, data engineers, lab scientists, product managers, and other stakeholders to understand their data processing needs and requirements; Communicate technical concepts and solutions effectively to both technical and non-technical audiences; Advocate for best practices in data processing and engineering; Manage expectations and ensure alignment across different teams.
  • Engineering Thought Leadership: Represent Lila's data platform work at external conferences; Deliver presentations, and write blog posts highlighting Lila's leadership in big data processing.
  • Scientist and Engineering Productivity: Drive innovative, agentic, and low-code solutions to deliver data interfaces - exploration, query, analytics, and ML/inference solutions at scale.

What You'll Need to Succeed

  • 12+ years of software development experience, with a focus on data processing at scale. 5+ years of experience leading senior engineers.
  • Experience with building on AWS/GCP primitives like S3 + Athena/BigQuery, and query engines.
  • Operated data platforms at petabyte scale with sub-second query latency requirements.
  • Experience managing data infrastructure supporting 100+ concurrent ML training and inference workloads.
  • Familiarity with LLM/AI-native data patterns - vector stores, embedding pipelines, pre/mid/post training.
  • Track record of building data platforms in high-growth or early-stage environments where speed-to-value mattered as much as long-term architecture.
  • Hands-on coding in Python and modern backend frameworks. Experience with infrastructure-as-code and containerized deployments (Kubernetes).
  • BS, MS, or Ph.D. in Computer Science or a related field of study.

Bonus Points For

  • Thought leadership in the community via presentations in conferences or blog posts.
  • Experience building and growing teams focusing on open source technologies.
  • Scientific data management and quality experience
  • Built self-service data products/platforms where developer experience was a first-class product concern.

Apply