Key Responsibilities and Required Skills for Data Engineering Lead

🎯 Role Definition

The Data Engineering Lead is responsible for owning and evolving the organization's data platform, designing and delivering scalable ETL/ELT workflows, leading a team of data engineers, and partnering with analytics, data science, and business stakeholders to turn raw data into reliable, governed, and high-performance data assets. This role balances hands-on technical delivery with people leadership, architectural stewardship, and cross-functional program execution to enable trusted analytics and data-driven decisions across the company.

📈 Career Progression

Typical Career Path

Entry Point From:

Senior Data Engineer with strong ownership of production data pipelines and architecture.
Data Platform Engineer experienced in cloud data platforms and infrastructure-as-code.
Analytics Engineering Lead (dbt/ETL-focused) moving into a broader platform leadership role.

Advancement To:

Director of Data Engineering
Head of Data Platform / VP of Data
Chief Data Officer (with cross-functional product/analytics leadership)

Lateral Moves:

Data Architect / Principal Data Engineer (individual contributor)
Machine Learning Engineering Lead (platform-focused)
Data Product Manager (platform or data service products)

Core Responsibilities

Primary Functions

Design, build, and operate end-to-end, high-throughput ETL/ELT data pipelines and streaming architectures that feed analytics, BI, and machine learning systems using technologies such as Spark, Kafka, Airflow, dbt, and cloud-native services.
Define and enforce best-practice data platform architecture, including logical and physical data models, partitioning strategies, change data capture (CDC), and schema evolution to ensure scalability and low-latency access to data.
Lead architecture decisions for cloud data warehouses and lakehouse solutions (e.g., Snowflake, Redshift, BigQuery, Databricks), evaluating cost, performance, and operational trade-offs for batch and streaming workloads.
Implement and operationalize observability for data pipelines (metrics, tracing, logs, and alerts) to achieve SLA-driven reliability and to quickly diagnose and remediate production issues.
Build and maintain robust data quality frameworks and automated testing for data pipelines (unit tests, integration tests, contract testing, anomaly detection), reducing downstream defects and ensuring trust in analytics.
Architect and enforce data governance controls, including access controls, data lineage, cataloging, metadata management, and PII/data-sensitivity handling in collaboration with security and compliance teams.
Drive the migration and consolidation of legacy ETL systems to modern cloud-native architectures, creating migration plans, timelines, and rollback strategies while minimizing business disruption.
Partner with product, analytics, and data science stakeholders to translate business questions into data engineering deliverables, prioritizing work by business impact and ROI.
Lead incident response and postmortem processes for platform outages, define remediation plans, and implement changes to prevent recurrence.
Establish and scale CI/CD pipelines for data platform code (SQL, Python, Spark, dbt) using GitOps patterns, automated testing, and release automation to accelerate safe deployments.
Manage platform cost optimization initiatives—right-sizing compute, tuning queries, and implementing lifecycle policies for storage tiers—to reduce cloud spend while maintaining performance.
Mentor and grow a team of data engineers: conduct 1:1s, career development planning, technical interviews, and performance reviews to build a high-performing, collaborative team.
Create and maintain technical documentation, runbooks, onboarding guides, and architecture diagrams to ensure knowledge transfer and reduce bus factor risks.
Define and track platform KPIs (pipeline latency, data freshness, error rates, query performance, on-call load) and report progress to senior leadership with recommendations for continuous improvement.
Introduce and operationalize Infrastructure as Code (IaC) for data infrastructure (Terraform, CloudFormation), standardizing environments and enabling reproducible deployments.
Drive secure data ingestion patterns from internal and external sources (APIs, event streams, SFTP, third-party feeds), ensuring reliable, idempotent, and auditable data collection.
Design for multi-tenant data access patterns and role-based access controls (RBAC), ensuring compliance with data privacy requirements and least-privilege principles.
Optimize large-scale distributed jobs (Spark, Flink, Dataflow) for performance and cost by profiling, tuning shuffle patterns, and choosing appropriate execution modes and cluster sizing.
Evaluate, pilot, and recommend new data technologies and managed services to improve time-to-value, reliability, and developer productivity across the platform.
Collaborate with Security and Legal teams to implement encryption, key management, tokenization, and audit logging for sensitive data flows and storage.
Define SLAs and SLOs for data products, work with consumers to establish expectations for data freshness, accuracy, and error handling contracts.
Drive cross-team initiatives to standardize data contracts, naming conventions, and reusable primitives (shared transforms, UDFs, schema templates) to reduce duplication and accelerate feature delivery.
Act as the primary technical point of contact for escalations involving complex data issues, working closely with site reliability engineering (SRE) and backend teams to remediate system-level problems.
Contribute to hiring plans, define competency frameworks for the data engineering organization, and create onboarding experiences for new hires to accelerate ramp time.

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.
Facilitate cross-functional workshops to align on data definitions, KPIs, and ownership of data domains.
Represent the data engineering team in vendor evaluations and contract discussions for third-party data tools and managed services.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: complex query optimization, window functions, CTEs, query profiling and performance tuning.
Cloud data platforms: hands-on experience with Snowflake, Redshift, BigQuery, or Databricks in production.
Distributed data processing frameworks: Spark (PySpark/Scala), Flink, or similar for batch and streaming.
Stream processing and messaging systems: Kafka, Kinesis, Pulsar — design for at-least-once/ exactly-once semantics and CDC patterns.
ETL/ELT orchestration: Airflow, Prefect, Dagster, or cloud-native schedulers; DAG design and dependency management.
Data modeling and warehousing: dimensional modeling, star/snowflake schemas, normalized/denormalized trade-offs.
Analytics engineering tooling: dbt or equivalent transformation frameworks and modular SQL development.
Programming: Python (pandas, PySpark), and familiarity with Scala/Java for JVM-based pipelines.
Infrastructure as Code and automation: Terraform, CloudFormation, GitOps, CI/CD for data code.
Observability and monitoring: Prometheus, Grafana, Datadog, New Relic, or cloud monitoring for pipeline health and performance.
Data governance and metadata tooling: Collibra, Alation, Amundsen, OpenMetadata, or custom catalog solutions.
Security and compliance: RBAC, IAM, encryption at rest/in transit, GDPR/CCPA considerations and PII handling.
Performance and cost optimization: query tuning, partitioning/clustering, cost-aware architecture decisions.
Containerization and orchestration basics: Docker, Kubernetes for deploying scalable data services.

Soft Skills

Leadership and people management: coaching, feedback, hiring, and career development.
Excellent stakeholder management: translate business needs into technical requirements and communicate trade-offs.
Strong written and verbal communication: produce clear documentation, runbooks, and executive summaries.
Strategic thinking and roadmap planning: align technical backlogs with business outcomes and KPIs.
Problem-solving and troubleshooting under pressure; calm, analytical approach to incidents.
Prioritization and time management: balance technical debt, platform reliability, and feature delivery.
Mentorship and knowledge sharing: run brown-bags, technical reviews, and promote best practices.
Collaboration and diplomacy: work across product, analytics, SRE, and security teams to drive consensus.
Change management: lead technology migrations and process improvements with minimal disruption.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Mathematics, Statistics, or related technical field.

Preferred Education:

Master’s degree in Computer Science, Data Science, Engineering Management, or MBA with strong technical background.

Relevant Fields of Study:

Computer Science
Software Engineering
Data Science / Analytics
Mathematics / Statistics
Information Systems

Experience Requirements

Typical Experience Range:

6–12+ years in data engineering, software engineering, or platform engineering roles.

Preferred:

8+ years of hands-on experience building data platforms and pipelines in production.
2–5 years in a technical leadership or people-management role with measurable team growth outcomes.
Demonstrated track record of delivering large-scale data projects, migrating systems to cloud data platforms, and implementing governance and observability at scale.