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Key Responsibilities and Required Skills for Data Warehouse Engineer

💰 $95,000 - $160,000

Data EngineeringBusiness IntelligenceAnalyticsCloud

🎯 Role Definition

A Data Warehouse Engineer designs, builds, and maintains scalable data warehouse solutions and ETL/ELT pipelines that transform raw data into reliable, high-performance analytical datasets. This role combines deep SQL and data modeling expertise with cloud platform knowledge (Snowflake, Redshift, BigQuery, Azure Synapse) and modern orchestration and transformation tools (Airflow, dbt, Matillion) to deliver trusted data for BI, analytics, and machine learning teams. The Data Warehouse Engineer partners with data analysts, product managers, and data scientists to translate business requirements into robust, documented, and governed data products.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Engineer / ETL Developer
  • Business Intelligence (BI) Developer or Analyst
  • Software Engineer with database experience

Advancement To:

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

Lateral Moves:

  • BI/Analytics Engineer
  • Machine Learning Engineer / MLOps Engineer
  • Data Governance or Data Strategy Specialist

Core Responsibilities

Primary Functions

  • Architect and build end-to-end data warehouse solutions on cloud platforms (Snowflake, AWS Redshift, Google BigQuery, Azure Synapse), including schema design, partitioning/clustering strategies, and cost/performance optimization to support high-concurrency analytics workloads.
  • Design, implement, and maintain robust ETL/ELT pipelines using orchestration tools (Airflow, Prefect, Dagster), transformation frameworks (dbt, Spark, SQL-based ELT), and integration tools (Matillion, Fivetran, Stitch), ensuring reliability, observability, and low latency.
  • Apply dimensional and normalized data modeling patterns (Kimball, Inmon, Data Vault) to create star schemas, slowly changing dimensions (SCD), conformed dimensions, and fact tables that support accurate reporting and fast query performance.
  • Author advanced SQL and data transformations to aggregate, cleanse, and enrich large datasets (terabytes), implementing window functions, CTEs, optimized joins, and materialized views to maximize analytical performance.
  • Implement data quality frameworks and automated testing (unit tests, schema tests, data reconciliation) using tools such as Great Expectations, dbt tests, and custom validation jobs to detect and remediate anomalies early.
  • Design and enforce ETL job orchestration, retry logic, SLA monitoring, and alerting for critical data pipelines to minimize downtime and ensure consistent data availability for downstream consumers.
  • Collaborate tightly with analytics, product, and data science teams to translate business KPIs and reporting requirements into scalable data models, documented transformation logic, and production-grade datasets.
  • Develop CI/CD pipelines for infrastructure-as-code and data code (dbt, SQL, Python) using Git, GitHub Actions, GitLab CI, or Jenkins to enable safe deployments, automated tests, and versioned releases of data assets.
  • Optimize data warehouse costs and performance through query profiling, storage tiering, clustering keys, materialized views, and workload management; provide recommendations and implement changes to balance cost and SLAs.
  • Build and maintain metadata, data catalogs, and lineage (using tools like Alation, Collibra, Amundsen) to improve discoverability, governance, and compliance across the enterprise data landscape.
  • Implement robust access controls, role-based security, row- and column-level masking, and encryption in the data warehouse to meet security and regulatory requirements such as GDPR, CCPA, or HIPAA.
  • Migrate legacy on-premise ETL and warehousing workloads to cloud-native architectures, planning cutovers, validating data parity, and ensuring minimal disruption to downstream reporting consumers.
  • Monitor pipeline health, job durations, and system metrics; diagnose intermittent failures, perform root-cause analysis, and implement long-term fixes and automation to reduce incident recurrence.
  • Create and maintain comprehensive technical documentation, runbooks, and onboarding guides for data models, schemas, ETL processes, and operational procedures to support team scaling and knowledge transfer.
  • Implement real-time or near-real-time ingestion patterns (CDC, Kafka, Kinesis) where required, designing streaming transformational logic or micro-batch processes to support operational analytics and event-driven use cases.
  • Design and operationalize data partitioning, archiving, and purge strategies to manage storage growth while preserving query performance and historical analysis capabilities.
  • Partner with data governance and compliance teams to define data lineage, retention policies, sensitive data discovery, and classification to ensure transparency and auditability.
  • Mentor and coach junior data engineers and cross-functional contributors, reviewing code, conducting design reviews, and promoting engineering best practices and standards across the team.
  • Define and instrument SLIs/SLOs for analytics datasets, and implement dashboards and runbooks to track data freshness, accuracy, and query performance for key reporting domains.
  • Evaluate, prototype, and recommend new data platform tools and architectures (lakehouse, columnar stores, serverless warehouses) and drive proofs-of-concept to improve scalability and developer productivity.
  • Collaborate with product stakeholders to prioritize data backlog, estimate work, and plan releases that align with business goals and data strategy.

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.
  • Provide subject-matter expertise for audits, compliance reviews, and vendor assessments.
  • Participate in cross-team initiatives to standardize metrics and semantic layers for consistent reporting.
  • Assist in onboarding analysts and business users to consume datasets and maintain self-service analytics capabilities.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: complex joins, window functions, CTEs, query optimization, and execution plan analysis.
  • Cloud data warehouses: hands-on experience with Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse.
  • ETL/ELT frameworks: design and implement pipelines with dbt, Apache Spark, Airflow, Matillion, Fivetran, or custom Python/Scala jobs.
  • Data modeling: dimensional modeling, Data Vault, star/snowflake schemas, SCD patterns, conformed dimensions, and normalization trade-offs.
  • Programming: Python for data engineering, with familiarity in Pandas, PySpark, or Spark SQL; Scala/Java experience is a plus.
  • Orchestration and scheduling: Apache Airflow, Prefect, Dagster, or cloud-native schedulers and their operational best practices.
  • Performance tuning: query profiling, materialized views, partitioning, clustering, vacuuming, and workload management.
  • CI/CD and version control: Git, automated testing, deployment pipelines for data code and infrastructure-as-code (Terraform, CloudFormation).
  • Observability and testing: data quality tools (Great Expectations), monitoring (Prometheus, Datadog), logging, and alerting.
  • Data governance and security: RBAC, token management, data masking, encryption at rest/in transit, and regulatory compliance.
  • Streaming/real-time ingestion (optional but valuable): Kafka, Kinesis, Debezium, CDC patterns, or Pub/Sub systems.
  • Metadata and lineage tools: Amundsen, Alation, Collibra, or native cloud metadata services.
  • SQL-based BI semantics: experience building semantic layers, aggregate tables, and metrics layers consumed by Looker, Tableau, Power BI, or Superset.

Soft Skills

  • Strong problem-solving and analytical thinking, with the ability to translate ambiguous business needs into technical solutions.
  • Excellent stakeholder communication: able to explain technical trade-offs and data implications to non-technical audiences.
  • Collaboration and teamwork: experience working in cross-functional agile squads with product managers, analysts, and SREs.
  • Attention to detail and quality orientation: insistence on automated tests, documentation, and reproducible builds.
  • Time management and prioritization: balance reliability, performance, and feature delivery in fast-paced environments.
  • Mentorship and knowledge sharing: coach junior engineers and promote best practices across teams.
  • Adaptability: comfortable evaluating new technologies and adjusting designs to evolving business needs.
  • Initiative and ownership: proactively identify bottlenecks, propose solutions, and drive end-to-end delivery.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Computer Science, Information Systems, Software Engineering, Mathematics, Statistics, or related technical field (or equivalent practical experience).

Preferred Education:

  • Master's degree in Computer Science, Data Science, Engineering, or MBA with a technical emphasis.
  • Relevant certifications (SnowPro, AWS/GCP/Azure Big Data, dbt Labs Certification) are a plus.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 3–7 years of hands-on data engineering or data warehousing experience.

Preferred: 5+ years of building and operating production data warehouses, with demonstrable experience on at least one cloud data platform (Snowflake, Redshift, or BigQuery) and modern transformation/orchestration tools (dbt, Airflow). Experience leading migrations, optimizing high-concurrency analytical workloads, and participating in data governance programs is highly desirable.

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