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Key Responsibilities and Required Skills for Director of Data

💰 $150,000 - $240,000

DataLeadershipAnalyticsEngineeringCloud

🎯 Role Definition

The Director of Data is a senior leadership position that owns the end-to-end data agenda: from strategy and governance to platform delivery, analytics, ML/AI enablement, and operationalization. This role partners with product, engineering, analytics, security, and the executive team to define the data roadmap, establish data governance and observability practices, manage cross-functional teams, and ensure reliable, compliant, and scalable data products and pipelines that accelerate business outcomes. The ideal candidate combines deep technical knowledge (data engineering, cloud data platforms, ML/AI lifecycle) with proven people leadership, stakeholder management, and a bias for delivering business value.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Head of Data Engineering
  • Senior Data Architect / Principal Data Engineer
  • Head of Analytics / Senior Analytics Engineering Manager

Advancement To:

  • Vice President of Data / VP of Data & Analytics
  • Chief Data Officer (CDO)
  • Chief Technology Officer (CTO) with a data focus

Lateral Moves:

  • Head of Machine Learning / ML Platform
  • Director of Business Intelligence / Analytics
  • Director of Data Platform / DataOps

Core Responsibilities

Primary Functions

  • Define, evangelize, and operationalize a multi-year data strategy and roadmap aligned with company objectives, prioritizing projects by business impact, ROI, and risk.
  • Lead, hire, mentor, and scale a cross-functional data organization including data engineers, data scientists, analytics engineers, platform engineers, ML engineers, and data product managers.
  • Own the design, delivery, and continuous improvement of a secure, scalable, and cost-effective cloud data platform (e.g., Snowflake, BigQuery, Redshift, Databricks) that supports batch and real-time workloads.
  • Drive architecture decisions for data ingestion, ETL/ELT, streaming pipelines (Kafka, Kinesis), orchestration (Airflow, dbt), and transformations to ensure low-latency, reliable data access.
  • Establish and enforce data governance, data quality, metadata management, master data management, and lineage practices to ensure trusted data for analytics and ML.
  • Partner with product and business leaders to translate business requirements into measurable data products, KPIs, dashboards, and self-service analytics capabilities.
  • Define and implement observability and monitoring for data pipelines and ML models, including SLAs, error reporting, alerting, and incident response for data incidents.
  • Create and manage the data platform budget, vendor selection (cloud providers, BI/ETL vendors, MLOps tools), licensing contracts, and third-party partnerships to optimize costs and capabilities.
  • Lead cloud data migration and modernization initiatives, including re-architecting legacy ETL to modern data warehouse/data lake architectures and ensuring secure, compliant migrations.
  • Oversee ML/AI productionization and MLOps practices: model deployment, versioning, feature stores, monitoring, and drift detection to reliably deliver ML at scale.
  • Champion data security, privacy, and compliance (GDPR, CCPA, SOC2) by collaborating with Security and Legal to implement access controls, encryption, and auditability.
  • Drive adoption of a data product mindset: define SLAs, SLIs, and customer (internal) contracts for data products and enable productized analytics consumption.
  • Partner with Engineering and DevOps to embed data best practices into CI/CD, infrastructure-as-code, containerization (Kubernetes), and automated testing for data pipelines.
  • Own and report on data organization KPIs (pipeline reliability, time-to-insight, adoption metrics, cost per query, data quality scores) to executive stakeholders.
  • Build and lead cross-functional programs to accelerate analytics and ML adoption across Sales, Marketing, Finance, Operations, and Product teams.
  • Develop and execute training, documentation, and enablement programs to increase data literacy and promote self-service analytics across the business.
  • Evaluate new data technologies, frameworks, and open-source tools; run PoCs, and make strategic recommendations for long-term tooling and architecture.
  • Resolve escalations and high-impact incidents involving data integrity, pipeline failures, or production analytics discrepancies with a strong focus on root cause and remediation.
  • Lead strategic initiatives to monetize data and analytics where appropriate (data products, analytics-as-a-service, internal cost allocation).
  • Define and implement data stewardship and domain ownership models to decentralize ownership while maintaining enterprise standards and interoperability.
  • Facilitate executive-level communication and storytelling using data: produce executive dashboards, briefings, and presentations that translate technical metrics into business outcomes.
  • Align hiring, org structure, and compensation with the long-term vision for data capability maturity and growth across domains and geographies.
  • Ensure redundancy, disaster recovery, and business continuity for critical data systems, with clear recovery time objectives (RTO) and recovery point objectives (RPO).

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL expertise and experience designing complex queries, performance tuning, and query optimization across large datasets.
  • Strong programming skills in Python and/or Scala/Java for data engineering, ETL, and ML pipelines.
  • Deep experience with cloud data platforms and data warehouses: Snowflake, BigQuery, Amazon Redshift, and/or Databricks.
  • Hands-on knowledge of data transformation and orchestration tools: dbt, Airflow, Luigi, or equivalent.
  • Experience building and operating streaming data architectures using Kafka, Kinesis, Pulsar, or similar technologies.
  • Proficiency with data modeling (dimensional modeling, star/snowflake schemas, data vault) and designing robust schema evolution strategies.
  • Familiarity with BI and analytics tools: Looker, Tableau, Power BI, Mode, or equivalent, including semantic layer design.
  • Experience implementing data governance, metadata management, cataloging (e.g., Amundsen, DataHub), and data quality frameworks.
  • Understanding of MLOps, model deployment pipelines, feature stores, and monitoring frameworks (MLflow, Seldon, Kubeflow).
  • Knowledge of data security, encryption, IAM, and compliance frameworks (GDPR, CCPA, HIPAA, SOC2).
  • Experience with infrastructure-as-code (Terraform, CloudFormation), containers (Docker), and orchestration (Kubernetes) in data platform contexts.
  • Familiarity with observability tooling for data (metrics, tracing, logging) and SLO/SLA design for data products.
  • Experience with CI/CD for data engineering (automated testing, linting, schema validation, deployment pipelines).
  • Strong quantitative abilities: statistics, A/B testing, causal inference, and applied machine learning fundamentals.
  • Vendor and contract evaluation skills for selecting cloud providers, ETL/ELT tools, BI platforms, and data security vendors.

Soft Skills

  • Strategic thinking and the ability to translate business strategy into a prioritized data roadmap.
  • Exceptional stakeholder management: influencing cross-functional leaders and aligning competing priorities.
  • Proven people leader: hiring, developing, and retaining high-performing technical teams across multiple disciplines.
  • Clear, concise executive communication and storytelling with data; able to present complex technical topics to non-technical audiences.
  • Strong project and program management skills: planning, risk management, change management, and delivering outcomes on time.
  • Collaborative mindset and ability to build trusting partnerships across Product, Engineering, Security, Legal, and Business teams.
  • Problem-solving orientation and bias for action; comfortable making trade-offs and decisions under uncertainty.
  • Coaching and mentorship capabilities to elevate technical and non-technical talent across the organization.
  • Ethical judgment regarding data usage, privacy, and compliance with legal and regulatory requirements.
  • Adaptability and systems thinking to operate effectively in fast-moving, growth-stage environments.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Data Science, Software Engineering, Mathematics, Statistics, or related field.

Preferred Education:

  • Master's degree or PhD in Data Science, Machine Learning, Computer Science, Business Analytics, Applied Mathematics, or an MBA with technical experience.

Relevant Fields of Study:

  • Computer Science
  • Data Science
  • Statistics / Applied Mathematics
  • Software Engineering
  • Business Analytics / Operations Research

Experience Requirements

Typical Experience Range:

  • 8–15+ years of relevant experience in data engineering, analytics, data science, or platform roles.

Preferred:

  • 10+ years of progressive experience with at least 3–5 years in a leadership role managing multiple data teams and delivering enterprise-scale data platforms.
  • Proven track record of designing and operating cloud-native data architectures, delivering production ML/analytics, and driving data governance and compliance programs.
  • Experience partnering with senior executives and translating data initiatives into measurable business impact, such as revenue growth, cost savings, or operational efficiency gains.
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