We are looking for a Senior Data Engineer to design, build, and operate modern data platforms at scale. You will work across cloud-native Azure infrastructure, Microsoft Fabric, Azure SQL, and AWS, building reliable pipelines, performant data models, and self-service analytics that drive real business decisions. A meaningful part of this role involves containerized workload execution -- designing and deploying pipeline jobs using Azure Container Apps Jobs for scheduled and event-driven data processing. This is a hands-on role suited for an engineer who thrives in ambiguity, values clean architecture, and moves comfortably between strategy and implementation.
RESPONSIBILITIESData Platform and Architecture
- Design and build scalable data pipelines using Python and cloud-native orchestration tools, including Azure Data Factory, Azure Container Apps Jobs, and Fabric Data Pipelines.
- Architect data solutions across Microsoft Fabric Warehouses, Azure SQL Database, and AWS (S3, Redshift), selecting the right tool for the workload.
- Implement Medallion/layered architecture patterns (Bronze to Silver to Gold) for structured, governed data delivery.
- Manage and optimize large-scale data warehouse environments with a focus on performance, cost, and maintainability.
Pipeline Development and Integration
- Develop Python-based ETL/ELT pipelines to ingest and transform data from APIs, flat files, databases, and SaaS platforms.
- Build and deploy containerized pipeline jobs using Azure Container Apps Jobs, including scheduling, scaling rules, secrets management via Azure Key Vault, and integration with Azure Container Registry.
- Build and maintain data movement between on-premises SQL Server environments and cloud targets.
- Design idempotent, fault-tolerant pipeline patterns with robust logging, alerting, and retry logic.
- Collaborate with analytics and reporting teams to deliver clean, well-documented data models for Power BI or similar BI tools.
Cloud Infrastructure and Operations
- Manage data infrastructure across Azure (Fabric, Azure SQL, Azure Data Lake, Key Vault, Container Apps, Container Registry) and AWS (S3, EC2, RDS/Redshift).
- Containerize data workloads using Docker; deploy and operate them as Azure Container Apps Jobs for scheduled batch processing and event-triggered pipeline execution.
- Implement infrastructure-as-code principles and version-controlled deployment practices using GitHub, Bicep or Terraform, and CI/CD tooling (Azure DevOps or GitHub Actions).
- Monitor platform health, optimize compute and storage costs, and enforce data security and access governance.
Collaboration and Engineering Excellence
- Partner with data analysts, BI developers, software engineers, and business stakeholders to translate requirements into technical solutions.
- Maintain thorough technical documentation: pipeline specs, data dictionaries, runbooks, and architecture diagrams.
- Champion engineering best practices: code reviews, testing, modular design, and reusable frameworks.
- Mentor junior engineers and contribute to team standards and knowledge sharing.
REQUIREMENTS- Python: fluent in writing production-grade pipelines, data transformations, and automation scripts.
- RDBMS: Advanced T-SQL and/or ANSI SQL; experience with SQL Server, Azure SQL DB, and cloud warehouse query engines (Redshift, Fabric).
- MS Fabric: Warehouses, Lakehouses, Data Pipelines, OneLake, and Fabric's unified analytics model.
- Azure ecosystem: Azure Data Factory, Azure SQL Database, Azure Data Lake Storage, Azure Key Vault, Azure Container Apps Jobs, Azure Container Registry, and related services.
- Containerization: Docker image development, container registry management, and deploying workloads as Container Apps Jobs with schedule and event triggers, scaling rules, and environment variable/secret injection.
- AWS data services: S3 for data lake storage, Redshift for cloud data warehousing.
- Data modeling: dimensional modeling, star/snowflake schema design, and entity-relationship modeling for both OLTP and OLAP workloads.
- Version control and DevOps: Git, GitHub, pull request workflows, and CI/CD pipelines.
- Data Visualization: Power BI, Tableau.
- Strong analytical problem-solving -- able to decompose ambiguous business problems into clean technical solutions.
- Clear written and verbal communication with both technical peers and non-technical stakeholders.
- Self-directed with strong attention to detail; comfortable owning work end-to-end.
Experience
- 5 to 8 years of hands-on data engineering experience in production environments.
- Proven track record designing and delivering data platforms on Azure and/or AWS.
- Demonstrated experience migrating or modernizing legacy on-premises data infrastructure to cloud-native solutions.
- Hands-on experience running workloads with Azure Container Apps Jobs or a comparable containerized job execution platform.
PREFERRED QUALIFICATIONS- Experience with MS Fabric in a production capacity, including Fabric Warehouses and OneLake integration.
- Familiarity with dbt (data build tool) or similar transformation frameworks.
- Exposure to streaming or near-real-time data ingestion patterns (Event Hub, Kafka, Kinesis).
- Experience with Workday, Adaptive Planning, or other ERP/FP&A source systems.
- Power BI experience including semantic model development, dataset optimization, or DirectQuery/Import mode tradeoffs.
- Agile/Scrum team experience; comfort working in iterative delivery cycles.
- Relevant cloud certifications: Microsoft Azure Data Engineer (DP-203), AWS Certified Data Analytics, or equivalent.
- Bachelor's degree in Computer Science, Information Systems, Data Science or a related field. In lieu of formal education, equivalent professional experience demonstrating the same depth of knowledge is accepted.
