Key Responsibilities and Required Skills for Data Lead

🎯 Role Definition

The Data Lead is a senior, hands-on leader who owns the end-to-end delivery of data products, data engineering, analytics, and governance. This role defines the data roadmap, architects scalable and secure data platforms (ETL/ELT, streaming, data warehousing), mentors and grows teams, and partners with business stakeholders to convert strategy into measurable outcomes. The Data Lead drives data quality, observability, and operational excellence while enabling self-serve analytics and advanced ML/AI use cases.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Data Engineer with cross-functional delivery experience
Analytics Manager / Senior Data Scientist transitioning to platform ownership
Head of BI or Senior Data Architect moving into broader data leadership

Advancement To:

Head of Data / Director of Data Engineering
VP of Data or Analytics
Chief Data Officer (CDO)

Lateral Moves:

Product Analytics Lead
ML Engineering Lead
Data Governance or Privacy Lead

Core Responsibilities

Primary Functions

Own the end-to-end data strategy and roadmap: define priorities for data ingestion, transformation, storage, governance, and analytics to align with company objectives and measurable KPIs.
Lead and manage a cross-functional data team (data engineers, analysts, ML engineers, and data architects), including hiring, performance reviews, career development, mentoring, and resource planning to scale capability and delivery.
Design and architect scalable, cost-effective data platforms and data pipelines using cloud-native technologies (AWS/GCP/Azure) that support batch and real-time streaming workloads.
Build and maintain the company data warehouse/lakehouse (e.g., Snowflake, BigQuery, Redshift, Databricks) and implement ELT best practices, including metadata management and documentation to enable self-serve analytics.
Implement robust data modeling and dimensional modeling patterns to ensure performant and reliable analytics datasets consumed by product, marketing, finance, and operations teams.
Lead the adoption and operationalization of modern data transformation tooling (dbt, Spark, Airflow, Prefect) and CI/CD best practices for data code, testing, and deployments.
Define and enforce data governance, lineage, cataloging, and master data management practices to ensure data quality, provenance, and regulatory compliance (GDPR, HIPAA where applicable).
Collaborate with product and business stakeholders to translate business requirements into technical specifications, prioritize initiatives, and deliver actionable dashboards and data products that drive revenue and retention.
Establish metrics, SLAs, and monitoring for data health, pipeline reliability, and job performance; proactively resolve incidents and reduce mean time to recovery (MTTR).
Lead design and implementation of streaming architectures (Kafka, Kinesis, Pub/Sub) for real-time analytics, event-driven systems, and feature ingestion for ML models.
Drive cost optimization and capacity planning across cloud data infrastructure, ensuring appropriate balance between performance, reliability, and cost.
Oversee implementation of data security best practices, access controls (RBAC), encryption, and logging to protect sensitive data and support audits.
Partner with Machine Learning and Data Science teams to operationalize models into production, including feature stores, model monitoring, and retraining pipelines.
Serve as primary liaison between engineering, product, finance, and business intelligence teams to align on KPIs, data definitions, and reporting standards.
Design and operationalize data observability and lineage tooling to provide transparency into the data ecosystem, enabling fast debugging and proactive quality improvements.
Drive tooling standardization and platform engineering to reduce technical debt and increase developer productivity (templated pipelines, data SDKs, developer docs).
Establish and track OKRs for the data organization, measure impact of data initiatives, and report progress to executive leadership with clear ROI and business metrics.
Manage vendor relationships and evaluate third-party data products (analytics platforms, MDM tools, ETL vendors) to augment internal capabilities.
Lead cross-team data migration and consolidation projects (schema changes, warehouse migrations, table re-orgs), coordinating release windows and validation plans to minimize business disruption.
Champion a culture of data literacy across the organization: run training, workshops, and regular office hours to enable non-technical teams to leverage data effectively.
Drive privacy-first design and collaborate with legal and security teams to maintain compliance posture for customer and employee data handling.
Stay current with industry trends (lakehouse architectures, LLMs, vector search, feature engineering frameworks) and evaluate emerging technologies for high-impact pilots and adoption.
Create and maintain clear documentation, runbooks, and onboarding materials for team members and stakeholders to ensure consistency and reduce bus factor.
Facilitate Agile delivery processes for the data org, including sprint planning, roadmap grooming, and prioritization to ensure timely delivery of high-impact features.

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.
Actively mentor junior engineers and analysts through 1:1s, code reviews, and brown-bag sessions.
Provide technical guidance on schema design, partitioning, indexing, and query performance tuning.
Coordinate cross-functional launches that require data instrumentation and analytics validation.
Lead post-incident reviews and implement action plans to prevent recurrence.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL proficiency for complex analytical queries, performance tuning, and data validation at scale.
Hands-on experience with at least one major cloud provider’s data ecosystem (AWS, GCP, or Azure) and cloud-native services (S3/Blob, BigQuery, Redshift, Snowflake).
Strong Python or Scala skills for ETL, data engineering, orchestration, and scripting tasks.
Expertise with data orchestration tools and workflow schedulers such as Airflow, Prefect, or Dagster.
Experience with modern transformation frameworks like dbt and building transformation tests and documentation.
Familiarity with streaming platforms (Kafka, Kinesis, Pub/Sub) and real-time processing frameworks (Spark Streaming, Flink).
Data modeling and dimensional modeling expertise for OLAP and analytical workloads.
Knowledge of data warehousing, lakehouse architecture, and query optimization strategies.
Experience with BI and visualization tools (Looker, Tableau, Power BI, or Mode) and delivering self-serve analytics.
Practical understanding of MLOps and productionizing ML models, feature stores, and model monitoring.
Experience implementing data governance, data catalogs (e.g., Amundsen, DataHub), lineage, and metadata management.
Strong understanding of data security, access control, encryption, and compliance frameworks (GDPR, HIPAA as applicable).
Familiarity with infrastructure-as-code and CI/CD for data (Terraform, GitHub Actions, Jenkins) and version control (Git).
Observability tooling experience (Prometheus, Grafana, Datadog, Monte Carlo, Great Expectations) for pipeline health and alerting.
Knowledge of containerization and orchestration (Docker, Kubernetes) as it relates to data workloads.
Experience evaluating and integrating LLM-based tooling, vector databases, or advanced analytics frameworks is a plus.

Soft Skills

Proven leadership and team-building skills with the ability to hire, mentor, and retain high-performing data talent.
Excellent stakeholder management and communication: translate technical tradeoffs to non-technical audiences and influence cross-functional priorities.
Strategic thinker with strong business acumen: prioritize initiatives that align with revenue, retention, or operational efficiency goals.
Strong problem-solving and analytical mindset with attention to detail and a bias for measurable outcomes.
Project management and delivery focus: able to drive cross-team projects to completion on time.
Adaptability and curiosity: rapidly learn new technologies and evaluate their applicability for the business.
Coaching and feedback orientation to develop team members and foster a growth culture.
Conflict resolution and negotiation skills to balance technical debt, delivery timelines, and stakeholder expectations.
Data-driven decision making and the ability to create clear metrics and dashboards to measure impact.
Collaboration and empathy to work effectively with product managers, engineers, legal, and business partners.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

Master's degree in Data Science, Computer Science, Business Analytics, or MBA for candidates with strong cross-functional leadership experience.
Certifications in cloud platforms (AWS/GCP/Azure), dbt, or data engineering specializations are beneficial.

Relevant Fields of Study:

Computer Science
Data Science / Machine Learning
Statistics / Applied Mathematics
Software Engineering
Information Systems / Business Analytics

Experience Requirements

Typical Experience Range:

5–12+ years in data-related roles with at least 2–4 years in a people leadership position.

Preferred:

7+ years building and operating production data platforms and 3+ years leading teams that deliver analytics, data engineering, or ML products. Proven track record of architecting cloud data solutions, implementing governance, and driving measurable business impact.