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Key Responsibilities and Required Skills for Data Engineering Team Lead

💰 $130,000 - $180,000

Data EngineeringLeadershipCloudBig DataAnalytics

🎯 Role Definition

We are seeking an experienced Data Engineering Team Lead who will combine hands-on engineering with people leadership to design, build, and operate robust data platforms and pipelines. The ideal candidate will own end-to-end data solutions — from ingestion and transformation to storage and serving — across cloud data platforms (AWS/GCP/Azure), streaming and batch processing (Kafka, Spark), and modern data warehouses (Snowflake, Redshift, BigQuery). This role emphasizes production reliability, data quality, observability, and cross-functional collaboration with analytics, product, and ML teams to translate business needs into technical solutions.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Senior Data Engineer with proven leadership or tech lead experience
  • Data Architect or Platform Engineer with cloud data platform focus
  • Lead Software Engineer or Backend Engineering Lead with big data exposure

Advancement To:

  • Head of Data Engineering / Data Platform
  • Director of Data / Director of Engineering (Data & Analytics)
  • VP of Data / Chief Data Officer (C-level roles)

Lateral Moves:

  • Machine Learning Engineering Lead
  • Data Product Manager or Analytics Engineering Lead
  • Platform Engineering or Site Reliability Engineering (SRE) Lead

Core Responsibilities

Primary Functions

  • Lead a team of data engineers to design, build, and operate scalable, resilient, and cost-effective ETL/ELT pipelines using modern orchestration tools (Airflow, dbt, Prefect), ensuring timely delivery of high-quality data to analytics and ML systems.
  • Architect and own the company’s cloud data platform (Snowflake, Redshift, BigQuery or equivalent) strategy, including storage patterns, partitioning, clustering, lifecycle policies, and cost optimization to support analytics, reporting and ML workloads.
  • Implement and enforce data modeling standards and best practices (dimensional modeling, star schema, data vault) to ensure consistent, performant and understandable data marts and semantic layers for business users.
  • Design and operate streaming data architectures (Kafka, Kinesis, Pub/Sub) for near real-time ingestion, transformation and delivery, including guarantees for message delivery, ordering, and backpressure handling.
  • Define and drive a roadmap for data platform improvements that balances technical debt reduction, performance optimization, security, and feature delivery in alignment with business priorities.
  • Mentor, hire and develop data engineering talent, conducting regular one-on-ones, career planning, performance reviews, and technical growth paths to build a high-performing team.
  • Collaborate with analytics, ML, product, and engineering stakeholders to translate business requirements into technical specifications, SLAs, and measurable success criteria for data products.
  • Establish and enforce data governance, access controls, and privacy practices (RBAC, encryption, PII handling, GDPR/CCPA considerations) to ensure compliance and secure data access across the organization.
  • Build and maintain CI/CD pipelines, automated testing frameworks, and deployment practices for data infrastructure and transformation code to ensure safe, repeatable releases.
  • Implement robust monitoring, observability and alerting for data pipelines and platform health (datadog, Prometheus, Grafana, Monte Carlo, Great Expectations) to reduce mean time to detection and resolution for data incidents.
  • Own incident response processes for data quality and pipeline failures, lead postmortems and root cause analyses, and ensure remediation and preventative actions are tracked and implemented.
  • Drive performance tuning and resource management (Spark optimizations, cluster sizing, query optimization, caching strategies) to minimize latency and cost while maximizing throughput.
  • Partner with security and infrastructure teams to manage IAM, VPC design, network security, and data encryption practices for data stores and messaging systems.
  • Standardize and document data engineering patterns, templates, and reusable components (ingestion frameworks, transformation macros, schema registries) to accelerate delivery and maintain consistency.
  • Lead the evaluation and adoption of new data technologies, vendors, and open-source projects, conducting cost/benefit analysis and proof-of-concept evaluations to modernize the data stack.
  • Define and track KPIs related to data reliability, freshness, latency, and adoption, and present regular updates to senior stakeholders and leadership to demonstrate value and progress.
  • Coordinate cross-functional releases with product, analytics, and platform teams to ensure data availability aligns with product launch timelines and business reporting cadences.
  • Ensure high-quality documentation of data lineage, schemas, transformations, and operational runbooks to improve traceability and facilitate onboarding of new team members.
  • Drive data democratization initiatives by building self-service tooling, well-documented APIs, and well-structured data catalogs that enable analysts and data scientists to access trusted datasets.
  • Lead cost governance efforts by monitoring cloud billing for data workloads, implementing cost controls, and recommending optimizations for storage and compute usage.
  • Champion observability and SLOs for downstream data consumers by implementing SLAs, data quality checks, and communication procedures when service degradation occurs.

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)

  • Expert-level SQL for complex analytics, performance tuning, window functions, CTEs, and query optimization for large datasets.
  • Strong Python and/or Scala programming skills for building data pipelines, UDFs, and infrastructure automation.
  • Hands-on experience with ETL/ELT frameworks and modern transformation tools (dbt, Airflow, Prefect, Luigi) and orchestration patterns.
  • Deep knowledge of cloud data platforms and services (AWS Redshift/S3/EMR, GCP BigQuery/Dataflow, Azure Synapse/Databricks) and infrastructure-as-code (Terraform, CloudFormation).
  • Proficiency with distributed compute engines like Apache Spark (PySpark/Scala) for batch and stream processing and cluster configuration/tuning.
  • Experience with streaming technologies and message brokers (Kafka, Kinesis, Pub/Sub) including schema evolution and delivery semantics.
  • Familiarity with data warehousing concepts, dimensional modeling, OLAP vs OLTP, and implementing high-performance analytical schemas.
  • Experience with modern data warehouse/cloud-native analytics (Snowflake, BigQuery, Redshift) and data lake architecture patterns.
  • Strong understanding of data quality frameworks, observability tools (Monte Carlo, Great Expectations), and automated testing strategies for data.
  • CI/CD and DevOps experience for data engineering (GitOps, automated deployments, containerization, Docker, Kubernetes).
  • Knowledge of data governance, metadata management, lineage tools, and privacy/compliance requirements (GDPR, CCPA).
  • Experience with performance tuning for large-scale ETL jobs, cost optimization strategies, and query profiling tools.
  • Familiarity with SQL-based BI/semantic layers and analytics tools (Looker, Tableau, Power BI) to support data consumers.
  • Experience designing APIs, data contracts, and versioning strategies for production data services.

Soft Skills

  • Proven leadership and people management skills: hiring, coaching, mentoring, and building high-performing teams.
  • Excellent stakeholder management with the ability to align technical roadmaps to business outcomes and communicate trade-offs clearly.
  • Strong written and verbal communication skills for documentation, runbooks, and executive-level reporting.
  • Strategic thinking and the ability to define and execute a multi-quarter data platform vision and roadmap.
  • Excellent problem-solving and incident management abilities with a focus on root-cause analysis and durable fixes.
  • Prioritization and time management skills to balance technical debt, feature delivery, and platform stability.
  • Collaborative mindset and experience working cross-functionally with product managers, data scientists, analysts, and security teams.
  • Adaptability and curiosity to evaluate new technologies, iterate on processes, and continuously improve the data stack.
  • Coaching-first approach to grow engineers’ technical and professional skills.
  • Customer-centric mentality to build data products that deliver measurable business impact.

Education & Experience

Educational Background

Minimum Education:

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

Preferred Education:

  • Master’s degree in Computer Science, Data Science, Software Engineering, or MBA with strong technical coursework.
  • Certifications in cloud platforms (AWS Certified Big Data / Data Analytics, Google Professional Data Engineer, Azure Data Engineer) are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 6 - 12 years in data engineering, software engineering, or platform engineering roles.

Preferred: 8+ years of hands-on data engineering experience with 2–5+ years in a team lead or engineering manager role, demonstrated success shipping large-scale data platforms, leading cross-functional initiatives, and mentoring engineers. Experience operating production data systems in cloud environments and measurable outcomes in data quality, reliability, and cost optimization preferred.

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