Data Platform Engineer
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Key skills for this role
About the Role
UrbaCon Contracting & Trading Company is seeking a Data Platform Engineer to design, build, and optimize enterprise data platforms for analytics, AI, and automation.
Key Skills for This Role
Responsibilities
- Design, build, and maintain enterprise data platforms including data lakes, lakehouses, and data warehouses
- Build and maintain batch and streaming ingestion frameworks for structured and unstructured data sources
- Implement orchestration tools such as Airflow, ADF, or Dagster
- Implement access controls, encryption, and platform security standards
- Monitor platform performance, workload usage, storage efficiency, and query responsiveness
Requirements
- Minimum 5–10 years of experience in Data Engineering, Data Platform Engineering, Big Data, or Data Warehousing roles
- Proven experience building cloud data platforms, pipelines, and integrated data ecosystems
- Experience supporting analytics, AI, automation, and enterprise reporting functions
- Bachelor's Degree in Information Technology or any related field
Full Job Posting
Job Summary
- The Data Platform Engineer is responsible for designing, building, operating, and optimizing the enterprise data platform that enables analytics, AI, reporting, and automation across the organization.
Job Responsibilities 1
- Design, build, and maintain enterprise data platforms including data lakes, lakehouses, and data warehouses.
- Manage cloud based data environments such as Databricks, Snowflake, BigQuery, Synapse, or equivalent platforms.
- Ensure scalability, reliability, resilience, and security of platform infrastructure across environments.
- Build and maintain batch and streaming ingestion frameworks for structured and unstructured data sources.
- Develop reusable ETL / ELT frameworks, templates, and standards for enterprise data movement.
- Integrate data from ERP, CRM, APIs, flat files, operational systems, and streaming sources.
- Implement orchestration tools such as Airflow, ADF, Dagster, or similar workflow schedulers.
- Establish CI/CD pipelines for data engineering deployments and automated testing processes.
- Promote infrastructure as code, containerization, and automation best practices.
- Implement access controls, encryption, and platform security standards for enterprise data assets.
- Enable metadata management, lineage capture, data cataloging, and discovery capabilities.
- Support governance frameworks, compliance requirements, and data quality monitoring controls.
Job Responsibilities 2
- Monitor platform performance, workload usage, storage efficiency, and query responsiveness.
- Optimize compute utilization, pipeline execution times, and operational costs.
- Troubleshoot platform incidents and ensure high availability with minimal disruption.
- Manage Dev, Test, and Production environments with controlled release practices.
- Implement backup, disaster recovery, retention, and recovery procedures for critical data assets.
- Ensure continuity planning for data platform operations and service resilience.
- Deliver curated datasets and optimized data structures for reporting, dashboards, AI models, and automation use cases.
- Enable feature ready datasets for machine learning and predictive analytics workloads.
- Support both real time and scheduled consumption models for enterprise users.
- Work closely with architects, analysts, AI teams, automation teams, and business stakeholders to improve platform capabilities.
- Recommend new technologies and engineering practices that enhance speed, scalability, and value delivery.
- Contribute to platform roadmaps, standards, and long term modernization initiatives.
Job Knowledge & Skills
- Modern Data Architectures: Strong knowledge of lakehouse, warehouse, medallion, and scalable data platform models.
- Data Engineering Tools: Expertise in ETL / ELT, orchestration, SQL, Python, Spark, and distributed processing.
- Governance Foundations: Understanding of metadata, lineage, catalogs, security, and quality frameworks.
- Performance Optimization: Strong capability in tuning pipelines, queries, storage, and compute efficiency.
- Stakeholder Service Mindset: Ability to deliver reliable platform services to multiple business consumers.
Job Experience
- Data Engineering Expertise: Minimum 5–10 years of experience in Data Engineering, Data Platform Engineering, Big Data, or Data Warehousing roles.
- Enterprise Platforms: Proven experience building cloud data platforms, pipelines, and integrated data ecosystems.
- Cross Functional Delivery: Experience supporting analytics, AI, automation, and enterprise reporting functions.
Education
- Bachelor's Degree in Information Technology or any related field
- Master's degree in Information Technology or any related field
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