Data Warehouse

We are seeking a Data Warehouse (DW) Data Engineer to design, build, and optimize a modern enterprise analytics platform. This individual will play a critical role in building, enhancing, and optimizing the enterprise data and analytics ecosystem. The role focuses on designing and developing ETL/ELT pipelines, Lakehouse/Warehouse models, and curated datasets that enable reporting and analytics across multiple business units.

This position works closely with BI Analysts, BI Developers, Architects, and business stakeholders to ensure high-quality, scalable, and governed data is available to support data-driven decision-making.

About First Factory

We are a software development company with over two decades of experience, boasting a dynamic team of 200+ professionals actively engaged in diverse projects across various industries. We invite you to join us on this journey as we thrive and embrace fresh challenges.

Key Responsibilities

Data Engineering & Pipeline Development

• Design, build, and maintain ETL/ELT pipelines using Microsoft Fabric (Pipelines, Dataflows Gen2, Notebooks, Spark) and legacy SSIS.

• Develop ingestion frameworks for flat files (CSV/Excel), APIs, SaaS platforms, cloud feeds, and partner data.

• Implement medallion architecture (Bronze, Silver, Gold) using Lakehouse (Delta Lake), Warehouse, and OneLake.

• Automate data transformations using SQL, PySpark, and Fabric Notebooks.

Data Modeling & Optimization

• Build and optimize star schema models, conformed dimensions, and fact tables for BI consumption.

• Implement incremental loads, SCD handling (Type 1/2), partitioning, Z-ordering, compaction, and other Delta Lake optimization techniques.

• Collaborate with BI Analysts to translate business requirements into performant data models.

Data Quality, Governance & Security

• Ensure end-to-end data quality through validation, reconciliations, profiling, and automated tests.

• Apply governance principles using Purview for lineage, classification, and data cataloging.

• Enforce Row-Level Security (RLS), object-level security, and access controls across Fabric datasets.

Cross-Team Collaboration

• Partner with BI Analysts and business stakeholders to understand KPIs, metrics, and reporting requirements.

• Work with Architects to establish data platform standards, naming conventions, folder structures, and version control patterns.

• Provide technical expertise during UAT, troubleshooting, and performance tuning.

Operational Excellence

• Monitor pipeline performance and proactively resolve pipeline failures.

• Implement CI/CD practices using Azure DevOps and Git for code and artifact promotion across Dev, Stage, and Prod environments.

• Contribute to documentation of data flows, data dictionaries, technical specifications, and workflows.

Requirements

• 5+ years of experience in data engineering, BI development, or data warehouse development.

• Strong SQL (T-SQL) skills for complex transformations, joins, window functions, and performance tuning.

• Hands-on experience with Microsoft Fabric (Lakehouse, Warehouse, OneLake, Pipelines, Dataflows Gen2, Notebooks).

• Experience with Delta Lake, Parquet, and medallion architectures.

• Proficiency with Python or PySpark for ingestion and transformation.

• Experience integrating REST APIs, SFTP feeds, SaaS connectors, and partner files.

• Strong understanding of dimensional modeling (Kimball), conformed dimensions, and data mart design.

• Familiarity with CI/CD workflows (Azure DevOps, Git).

• Excellent troubleshooting, debugging, and performance optimization skills.

• Ability to work in a fast-paced, dynamic environment while managing multiple priorities.

• 3–5 years of experience with SSMS, SSDT, SSIS, SSAS, and SSRS.

Nice to have

• Experience in retail or QSR (Quick Service Restaurant) data ecosystems.

• Experience working with POS, labor, inventory, marketing, or supply chain data.

• Experience with Power BI, including semantic models and performance considerations.

• Exposure to Azure Data Factory, Synapse, or Databricks.

• Experience with workflow orchestration and metadata-driven frameworks.

• Knowledge of data governance tools (Purview), data security best practices, and lineage management.

• Strong written communication skills for documentation and cross-functional alignment.