Key Responsibilities and Required Skills for Director of Data Engineering

🎯 Role Definition

The Director of Data Engineering leads strategy and execution for the company's data platform, ensuring reliable, secure, and scalable data pipelines, data warehouse/lakehouse architecture, and platform services that accelerate analytics, machine learning, and product development. This role combines technical leadership, team building, stakeholder management, and operational ownership to deliver high-quality data products and enable data-driven decisions across the enterprise.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Data Engineer with proven platform delivery and team lead experience
Engineering Manager / Technical Lead in data or backend systems
Head of Analytics Engineering or Principal Data Architect

Advancement To:

VP of Data & Analytics
Chief Data Officer (CDO)
Head of Machine Learning / Head of Data Platforms

Lateral Moves:

Director of Machine Learning Infrastructure
Director of Analytics Engineering
Director of Platform Engineering (Data-focused)

Core Responsibilities

Primary Functions

Define and own the end-to-end data engineering and platform strategy—including data ingestion, storage, processing, orchestration, quality, and observability—aligned to product and business priorities and measurable KPIs.
Lead architecture design and roadmap for cloud-native data platforms (data lake, lakehouse, data warehouse) ensuring modular, secure, cost-efficient, and scalable solutions across AWS, GCP, or Azure.
Architect and operationalize resilient ETL/ELT patterns and pipelines using modern tools (Spark, Flink, dbt, Airflow, Dagster, etc.) to support near-real-time streaming and batch analytics.
Build and scale a high-performing data engineering organization: recruit, mentor, set career paths, run performance reviews, and create a culture of ownership, quality, and continuous improvement.
Collaborate with product, analytics, ML, security, and infrastructure leaders to translate business needs into prioritized data platform initiatives and delivery commitments.
Establish and enforce data governance, lineage, metadata management, access controls, and compliance practices to meet regulatory and internal security standards.
Drive platform standards for data modeling, cataloging, semantic layers, and shared data contracts to ensure consistency, discoverability, and reusability of data assets.
Implement monitoring, alerting, and SLOs for data pipelines, ETL jobs, and platform services; lead incident response and postmortems to improve reliability and reduce mean time to recovery.
Optimize data platform cost and performance through capacity planning, right-sizing, storage tiering, and cloud cost controls while maintaining SLAs for consumers.
Lead adoption and integration of observability, lineage, and quality tooling (Great Expectations, Monte Carlo, OpenLineage) to proactively surface data issues and SLA breaches.
Own vendor selection and management for critical platform components (data warehouse, streaming, OLAP engines, orchestration) and manage third-party contracts and budgets.
Drive CI/CD and infrastructure-as-code practices for data pipelines and platform components (Terraform, CloudFormation, Helm) to ensure repeatable, auditable deployments.
Partner with ML Engineering and Data Science to deliver feature stores, model data pipelines, and productionize ML workflows with strong ML observability and reproducibility.
Define and track metrics for data product adoption, latency, freshness, query performance, and data quality; present regular program updates to executive stakeholders.
Champion data democratization initiatives, self-service tooling, and internal developer experience to reduce time-to-insight for analysts and product teams.
Create and run a multi-year roadmap for modernization efforts (e.g., migration to Snowflake/BigQuery/Redshift, adoption of lakehouse patterns) with clear milestones and measurable outcomes.
Set technical standards and code review practices across data engineering teams; ensure adherence to software engineering best practices including testing, modularity, and documentation.
Facilitate cross-functional prioritization and negotiation, balancing technical debt, innovation, and delivery for high-impact business outcomes.
Foster an inclusive engineering culture that emphasizes psychological safety, knowledge sharing, pair programming, and continuous learning for the data organization.
Act as a hands-on technical advisor for complex platform issues, performing architecture reviews, technical deep-dives, and proof-of-concepts where needed to de-risk major initiatives.

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.
Develop and maintain documentation for platform architecture, data flows, and operational runbooks.
Run workshops and training sessions to raise platform adoption and best practices across analytics and product teams.
Manage budget, headcount planning, and vendor spend for the data engineering organization.
Coordinate with Security and Privacy teams to implement data masking, encryption, and consent management for sensitive datasets.

Required Skills & Competencies

Hard Skills (Technical)

Cloud Data Platforms: Hands-on experience designing and operating data platforms on AWS, GCP, or Azure (S3/GCS, Redshift/Snowflake/BigQuery, Databricks).
Data Pipeline Frameworks: Deep knowledge of Spark, Flink, Beam, or equivalent for batch and streaming processing.
Orchestration & Workflow: Production experience with Airflow, Dagster, Prefect, or equivalent orchestration systems.
Data Modeling & Warehousing: Strong experience in dimensional modeling, star/snowflake schemas, and modern lakehouse/warehouse design patterns.
ELT/ETL Tools: Practical fluency with dbt, Fivetran, Stitch, Matillion, or custom ETL frameworks.
Streaming & Messaging: Experience with Kafka, Pub/Sub, Kinesis, or equivalent streaming platforms and event-driven architectures.
Metadata & Governance: Expertise implementing data cataloging, lineage, quality frameworks (e.g., Amundsen, DataHub, Collibra).
Observability & Testing: Proficiency with data quality, alerting, SLOs, test suites (unit/integration) and data monitoring tools (Monte Carlo, Great Expectations).
Infrastructure-as-Code & CI/CD: Experience with Terraform, CloudFormation, GitOps, and automated deployment pipelines for data infra.
SQL & Programmatic Data Access: Advanced SQL, familiarity with Python/Scala/Java for pipeline development, and performance tuning of queries.
Scalability & Performance: Proven record of designing systems for high throughput, low latency, and multi-tenant use.
Security & Compliance: Knowledge of IAM, encryption, PII handling, and regulatory requirements (GDPR, CCPA, HIPAA where applicable).
Cost Optimization: Skills in cloud cost analysis, storage tiering, and engineering change management to control platform spend.
ML/Feature Engineering Support: Familiarity with feature stores, model deployment pipelines, and MLOps tooling.
Vendor & Contract Management: Experience evaluating and negotiating with data vendor solutions and managed services.

Soft Skills

Strategic Leadership: Ability to translate business strategy into technical roadmaps and measurable outcomes.
Cross-functional Communication: Clear communicator with product, analytics, security, and executive stakeholders.
People Management: Coaching, mentoring, performance management, and talent development experience.
Prioritization & Decision-Making: Skilled at balancing competing priorities, technical debt, and delivery timelines.
Problem Solving & Systems Thinking: Strong at breaking down complex technical problems and designing pragmatic solutions.
Influence & Negotiation: Capacity to influence across teams and secure buy-in for platform initiatives.
Operational Comfort: Bias for action, methodical runbook creation, and calm leadership during incidents.
Change Management: Ability to lead organizational change and drive adoption of new tools and processes.
Customer Orientation: Focus on internal consumers of the data platform—analysts, data scientists, and product teams.
Continuous Learning: Commitment to staying current with evolving data technologies and best practices.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Mathematics, or related field.

Preferred Education:

Master’s degree in Computer Science, Data Science, Business Analytics, or MBA preferred for strategic/large-enterprise roles.
Professional certifications in cloud platforms (AWS/GCP/Azure) or data tools (Databricks, Snowflake) are a plus.

Relevant Fields of Study:

Computer Science
Data Engineering / Data Science
Software Engineering
Information Systems
Mathematics, Statistics, or Applied Sciences

Experience Requirements

Typical Experience Range: 8–15+ years in software or data engineering roles with progressive technical leadership.

Preferred:

5+ years managing and scaling data engineering teams and delivering enterprise-grade data platforms.
Demonstrated track record in cloud migrations, building lakehouse/warehouse architectures, and enabling analytics/ML productionization.
Experience working in agile environments, cross-functional product teams, and presenting platform strategy to senior executives.