Key Responsibilities and Required Skills for Data Warehouse Developer

🎯 Role Definition

As a Data Warehouse Developer, you will be responsible for designing, building, and maintaining enterprise‑scale data warehouse platforms that integrate data from multiple sources, enable analytic and reporting needs, and support data‑driven decision‑making across the organization. You will collaborate with business stakeholders, data engineers, business intelligence analysts and infrastructure teams to implement ETL/ELT pipelines, dimensional models (star and snowflake schemas), data marts and performance‑tuned database objects. Your mission is to ensure data accuracy, scalability, availability and optimal query performance, while also driving best practices in data warehousing, governance and documentation.

📈 Career Progression

Typical Career Path

Entry Point From:

ETL Developer or Data Integration Specialist
Business Intelligence (BI) Developer or Reporting Analyst
SQL/Database Developer transitioning into data warehousing

Advancement To:

Senior Data Warehouse Developer or Data Warehouse Architect
Lead Data Engineer or Analytics Platform Lead
Data Architect, Director of Data & Analytics or BI Infrastructure Manager

Lateral Moves:

Data Engineer (specialising in pipelines and ELT)
BI/Analytics Engineer focusing on dashboards and visualisation
Data Architect specialising in cloud data warehouses or big‑data platforms

Core Responsibilities

Primary Functions

Design and implement enterprise‑data‑warehouse architectures, including staging areas, enterprise data warehouse (EDW), data marts and dimensional models (star/snowflake schemas) aligned with business reporting needs.
Develop, maintain and optimise ETL/ELT pipelines (Extract, Transform, Load) to ingest data from heterogeneous sources into the warehouse environment, ensuring high data quality, efficiency and accuracy.
Collaborate closely with business analysts, data scientists and stakeholders to define data requirements, translate business needs into technical solutions and ensure that the data warehouse supports analytics and decision‑making.
Optimize the performance of the data warehouse: tune queries, indexes, partitions, manage load schedules, monitor and improve latency of data delivery to meet business SLAs.
Design and implement robust data models, define logical and physical data schemas, maintain master data, conform dimensions, and manage slowly changing dimensions and fact tables in dimensional modelling.
Ensure data integrity, consistency, completeness and security in the data warehouse: implement data profiling, data validation, reconciliation and audit frameworks.
Design and maintain documentation such as data dictionaries, data flow diagrams, process definitions, metadata management and architecture diagrams to support sustainable warehouse operations.
Monitor production data‑warehouse environments on an ongoing basis, identify and rapidly resolve issues (ETL failures, data quality issues, query performance bottlenecks) and conduct root‑cause analysis.
Lead and participate in data‑warehouse build‑out or migration projects (e.g., legacy to modern platform, on‑premises to cloud, Hadoop/Spark integrations) while managing schedule, quality, and business impact.
Collaborate with DBA, infrastructure and cloud teams to deploy, scale and maintain data‑warehouse infrastructure, including database servers, storage, compute, partitioning strategies, and big‑data/parallel processing systems.
Implement best practices for change management, version control, CI/CD pipelines and automated deployment of data‑warehouse artifacts (ETL packages, scripts, data models).
Support reporting and analytics teams by creating data marts, aggregate tables, ODS (operational data stores) and OLAP structures to serve self‑service BI, dashboards and advanced analytics.
Implement data governance, retention policies, compliance and security measures within the data‑warehouse environment to meet regulatory and internal standards.
Mentor and coach junior data‑warehouse developers or BI engineers, lead peer code‑reviews, promote standards for data‑warehouse development, modelling and deployment.
Stay current with emerging data‑warehouse technologies (Snowflake, Redshift, BigQuery, Azure Synapse), big‑data frameworks (Hadoop, Spark) and evaluate their impact on the organisation’s data strategy.
Collaborate in agile development teams: participate in sprint planning, backlog grooming, delivery cycles and ensure alignment of data‑warehouse deliverables with business objectives.
Work on data‑warehouse cost‑optimisation: control compute/storage costs, manage resource utilisation, and implement efficient data‑warehouse architectures for scalability and cost‑effectiveness.
Conduct regular audits and assessments of data‑warehouse health, document findings, propose improvements to architecture, processes and tools and drive continuous improvement.
Integrate external systems, APIs or third‑party data sources into the data‑warehouse ecosystem, mapping and converting data, ensuring data quality and alignment with existing sources.
Design and implement metering, monitoring and alerting for the data warehouse environment: data‑load monitoring, job failures, SLA breaches, performance metrics and dashboards for operational oversight.
Support ad‑hoc data requests and exploratory data analysis by preparing data extracts, sandbox environments or data‑warehouse views to enable advanced analytics.

Secondary Functions

Contribute to the organisation’s data‑warehouse strategy, roadmap and architecture reviews.
Collaborate with business units to translate analytic needs into tangible data‑warehouse and BI deliverables.
Participate in sprint planning and agile ceremonies with data‑engineering, BI, and product teams.

Required Skills & Competencies

Hard Skills (Technical)

Expert proficiency in SQL (T‑SQL, PL/SQL) including complex queries, stored procedures, functions, indexes, partitions and query optimisation.
Strong experience with ETL/ELT tools and frameworks such as SSIS, Informatica, Talend, Apache NiFi, ODI, or custom scripting.
Solid knowledge of data‑warehouse modelling techniques (star schema, snowflake schema, slowly changing dimensions, fact/dim tables) and data architecture.
Experience with data‑warehouse platforms and technologies such as Snowflake, Amazon Redshift, Google BigQuery, Teradata, Azure Synapse Analytics.
Proficiency with scripting/programming languages (Python, Java, Scala) and database platforms to automate data pipeline tasks.
Knowledge of big‑data frameworks (Hadoop, Spark) and integration of large‑scale data systems where applicable.
Experience with data quality, data‑governance frameworks, metadata management, and auditing of data flows and data sets.
Familiarity with cloud infrastructure, storage, compute, data‑lakes and warehousing in cloud or hybrid environments.
Solid experience with version control, CI/CD pipelines, staging/deployment environments, monitoring and job orchestration frameworks.
Strong documentation skills: ability to maintain data dictionaries, data‑flow maps, service‑level metrics, technical designs and operational run‑books.

Soft Skills

Excellent analytical and problem‑solving capability: able to dissect complex data issues, propose solutions, perform root‑cause analysis and optimise systems.
Strong communication skills: able to articulate technical concepts to non‑technical stakeholders, build consensus and drive change with business teams.
Collaboration and teamwork: effective in cross‑functional settings including BI analysts, data scientists, infrastructure and business stakeholders.
Attention to detail and accuracy: ensure high‑quality data delivery, reliably structured models and maintain standards across the data‑warehouse environment.
Adaptability and continuous learning: committed to staying current with evolving data‑warehouse technology, cloud services and analytics demands.
Time management and prioritisation: able to handle multiple project deadlines, urgent data‑requests and backlog of enhancement tasks.
Ownership and accountability: responsible for end‑to‑end deliverables, from design through implementation to production and ongoing maintenance.
Strategic mindset: able to align data‑warehouse work with broader business strategy, proposing improvements and optimisations.
Mentoring and leadership: guide junior developers, share knowledge, uphold best practices and contribute to development of the data‑team.
Resilience under pressure: work through high‑availability, 24/7 or mission‑critical data‑warehouse operations and manage escalations when necessary.

Education & Experience

Educational Background

Minimum Education:

Bachelor’s degree in Computer Science, Information Systems, Software Engineering, Data Science or a related technical discipline.

Preferred Education:

Master’s degree in Data Engineering, Business Intelligence, Analytics or equivalent professional certification(s) in ETL/data‑warehousing technologies.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range:

3 to 5 years of experience working in data‑warehouse development, ETL pipeline design, database modelling and analytics environments.

Preferred:

5+ years of progressive experience designing, delivering and maintaining enterprise data‑warehouse solutions, ideally across cloud and legacy platforms, with leadership or mentoring responsibilities.