Key Responsibilities and Required Skills for Warehouse Engineer

🎯 Role Definition

A Warehouse Engineer (Data Warehouse / Analytics Engineer) is responsible for designing, building, and maintaining scalable, reliable data infrastructure that enables analytics, reporting, and machine learning. The role spans end-to-end data pipeline development (ETL/ELT), data modeling, performance optimization, data governance, and cross-functional collaboration with product, analytics, and engineering teams. Success is measured by the accuracy, timeliness, and cost-efficiency of data delivery for decision-making and operational processes.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst transitioning into engineering-focused data work
ETL Developer or BI Developer with strong SQL and pipeline experience
Software Engineer interested in data platforms and analytics

Advancement To:

Senior Warehouse Engineer / Lead Data Engineer
Data Platform Architect / Analytics Engineering Manager
Head of Data / Director of Data Engineering

Lateral Moves:

Machine Learning Engineer (with additional ML specialization)
BI / Analytics Engineering (dashboard and reporting lead)
Site Reliability Engineer for data infrastructure

Core Responsibilities

Primary Functions

Design, implement and maintain robust ETL/ELT pipelines to ingest, transform, and replicate data from diverse sources (APIs, transactional databases, event streams like Kafka, third-party data providers) into the enterprise data warehouse using tools such as dbt, Airflow, Spark, or native cloud services.
Build and maintain dimensional data models, star schemas, and normalized models to support reporting, BI, and ML use cases while ensuring consistency across business domains.
Optimize data warehouse performance (query tuning, partitioning, clustering, indexing) in cloud warehouses such as Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse to reduce latency and cost.
Develop and enforce data quality checks, validation frameworks, and automated testing pipelines (unit, integration, and regression tests) to ensure high data integrity and trustworthiness.
Implement and maintain metadata management, lineage tracking, and cataloging solutions to provide transparency for data consumers and to support regulatory compliance and auditing.
Architect and own CI/CD pipelines for analytics code, dbt models, SQL, and infrastructure-as-code (Terraform, CloudFormation), enabling reproducible deployments and rapid iteration.
Collaborate with data scientists, analysts, product managers, and business stakeholders to translate business requirements into technical designs, data contracts, and measurable SLAs.
Lead migration and consolidation projects from legacy ETL systems to modern ELT patterns and cloud-native warehouses, minimizing downtime and preserving data fidelity.
Monitor production pipelines and warehouse health, implement observability (logs, metrics, tracing), alerting, and incident response processes to meet operational SLAs.
Design and enforce data governance, access controls, row/column-level security, and encryption practices to protect sensitive information and meet compliance requirements (GDPR, CCPA, SOC2).
Implement cost monitoring and optimization strategies for storage and compute (auto-suspend, clustering, resource classes, and query optimization) to control cloud spend related to data workloads.
Prototype and evaluate new data technologies, ETL frameworks, and query engines, making recommendations and conducting POCs to improve scalability and developer productivity.
Create and maintain comprehensive documentation, runbooks, and onboarding materials for data models, pipelines, and platform usage to shorten time-to-value for internal data consumers.
Mentor junior data engineers and analytics engineers, conduct code reviews, and promote engineering best practices such as modular design, reusability, and observability.
Design streaming and near-real-time ingestion patterns (Kafka, Kinesis, Pub/Sub) and event-driven architectures for time-sensitive analytics and operational dashboards.
Implement schema evolution strategies and change-data-capture (CDC) pipelines from OLTP systems to preserve historical accuracy and support slowly changing dimensions.
Partner with security and infrastructure teams to harden network configurations, IAM roles, and service accounts for secure, least-privilege access to data systems.
Establish SLAs for data freshness, completeness, and accuracy; build monitoring and dashboards to report against these KPIs to stakeholders.
Troubleshoot complex data issues end-to-end, performing root cause analysis, remediation, and post-incident reviews with action plans to prevent recurrence.
Drive cross-team initiatives to standardize naming conventions, data contracts, and reusable components (macros, packages, shared models) to reduce duplication and accelerate delivery.
Lead capacity planning and archival strategies for large-scale datasets, balancing accessibility for analytics with cost and retention policies.
Support the onboarding of third-party analytics tools and integrations (Looker, Tableau, Power BI, Amplitude) ensuring data models expose performant, analytics-ready schemas.
Participate in architecture reviews and design sessions to align data platform evolution with company growth and product roadmap.

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.
Assist with vendor evaluations and manage relationships for ETL/ELT tools, data catalogs, or managed warehouses.
Provide occasional on-call support for data platform incidents and customer-impacting outages.
Help define KPIs and metrics instrumentation in collaboration with analytics and product teams.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: writing complex queries, window functions, CTEs, performance tuning, and query plan analysis.
ETL/ELT frameworks: practical experience with dbt, Apache Airflow, Dagster, or equivalent orchestration tools.
Cloud data warehouses: hands-on with Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse.
Programming: Python (pandas, sqlalchemy), Scala, or Java for building transformations, UDFs, and pipeline logic.
Big data processing: familiarity with Spark, Presto/Trino, or Beam for large-scale transformations.
Streaming & CDC: Kafka, Kinesis, Pub/Sub, Debezium, or similar technologies for real-time ingestion.
Data modeling: dimensional modeling, star/snowflake schemas, normalization/denormalization strategies, and slowly changing dimensions.
DevOps/Infrastructure-as-Code: Terraform, CloudFormation, Kubernetes basics for deploying platform components.
Data governance & security: IAM, RBAC, encryption at rest/in transit, PII handling, and compliance best practices.
Monitoring & observability: Prometheus, Grafana, Datadog, or native cloud monitoring for pipelines and warehouse metrics.
Testing & CI/CD: unit/integration tests for data, Git-based workflows, automated deployment pipelines.
APIs & integrations: REST/GraphQL, JDBC/ODBC integrations for source and sink systems.
Metadata & lineage: tools and practices for tracking data lineage, cataloging (e.g., Amundsen, Data Catalog, Alation).
Cost optimization: experience analyzing and reducing cloud compute/storage costs for analytic workloads.
Terraform / IAM policy authoring and security posture reviews.

Soft Skills

Strong communication and stakeholder management: translate technical trade-offs to business audiences and negotiate data contracts.
Problem-solving and debugging mindset: structured RCA and pragmatic remediation skills.
Collaboration and cross-functional leadership: work effectively with product, analytics, and infra teams.
Time management and prioritization: balance feature development, tech debt, and operational support.
Mentoring and knowledge-sharing: provide coaching, documentation, and best-practice guidance for engineering peers.
Business acumen: understand how data products drive decisions and influence product metrics and KPIs.
Adaptability and continuous learning: stay current with evolving data technologies and cloud features.
Attention to detail: strong focus on data correctness, reproducibility, and auditability.
Project ownership and accountability: end-to-end delivery focus from design through production support.
Customer-centric mindset: internal data consumers and external regulatory requirements inform priorities.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Computer Science, Engineering, Information Systems, Mathematics, Statistics, or a related technical discipline (or equivalent practical experience).

Preferred Education:

Master’s degree in Computer Science, Data Science, Analytics, or a related field is advantageous.
Certifications such as Snowflake Advanced Architect, Google Professional Data Engineer, AWS Big Data Specialty, or dbt Fundamentals are a plus.

Relevant Fields of Study:

Computer Science / Software Engineering
Data Science / Applied Mathematics
Information Systems / Management Information Systems
Statistics / Operations Research

Experience Requirements

Typical Experience Range:

3–8+ years of professional experience in data engineering, analytics engineering, ETL development, or similar roles.

Preferred:

5+ years building and operating cloud-based data warehouses with demonstrable projects in Snowflake/Redshift/BigQuery.
Prior experience designing data platforms for analytics and machine learning at scale, with measurable impact (reduced query latency, improved data freshness, cost savings).
Experience mentoring engineers and leading cross-functional projects.