Data Systems Engineer & Data Architect

Polar Service Centers•Houston, TX

60d

About The Position

Discover a career at Depot Connect International (DCI), a global leader in the Tank/ISO Tank Container Services and Tank Trailer Parts industry. We're more than just a service provider; we're a unified team combining the expertise of industry leaders Quala, Boasso Global, and Polar Service Centers (PSC). Headquartered in Tampa, Florida, with over 160 locations worldwide, our team of over 3,500 employees excels in offering a multitude of mission-critical services. Data Architect & Data Engineer (MDM/A2A Focus) This role combines the strategic oversight of a Data Architect with the practical, hands-on implementation skills of a Data Engineer, focusing heavily on Master Data Management (MDM) and Application-to-Application (A2A) integrations. The ideal candidate will be responsible for designing, building, and optimizing the data infrastructure and pipelines that ensure data quality, consistency, and efficient exchange across the enterprise. 1. Data Architect Responsibilities (Strategy & Design) As the Data Architect, you'll be the visionary for the data landscape, particularly in MDM and integration domains. MDM Strategy & Design: Define and govern the Master Data Management (MDM) strategy, roadmap, and architecture (e.g., Customer, Product, Vendor, etc.). Design the conceptual, logical, and physical data models for MDM solutions, ensuring data lineage and governance are clearly defined. Select and evaluate appropriate MDM technologies and tools. Integration Architecture (A2A): Design scalable and robust Application-to-Application (A2A) integration architectures using various patterns (e.g., API, messaging, ETL/ELT). Establish technical standards and best practices for data integration, security, and performance. Data Governance & Quality: Collaborate with Data Governance teams to define data standards, policies, and quality rules. Design systems for continuous data quality monitoring and remediation. Technology Leadership: Provide technical leadership and mentorship to data engineering teams. Drive adoption of modern data architecture principles (e.g., Data Mesh, Data Fabric). 2. Data Engineer Responsibilities (Hands-on Implementation) As the Data Engineer, you'll be responsible for the hands-on building and operationalization of data solutions. Data Pipeline Development: Design, build, and maintain robust, scalable, and efficient ETL/ELT pipelines to ingest, transform, and load data from various source systems into the MDM hub and analytical platforms. Develop and optimize data flows for real-time and batch A2A integrations. MDM Implementation: Hands-on configuration, development, and testing of MDM platform components (e.g., matching rules, survivorship logic, data quality checks). Develop custom connectors and services to synchronize master data across enterprise systems. Cloud & Big Data Tools: Implement data solutions using cloud-native services (e.g., AWS, Azure, GCP) including services like Kafka/Kinesis, Snowflake/Redshift/BigQuery, Spark/Databricks, and managed database services. Write high-quality, maintainable code in languages like Python or Scala. Automation & Monitoring: Implement CI/CD practices for data pipelines and infrastructure-as-code (IaC). Set up comprehensive monitoring and alerting for data quality, pipeline performance, and health integration.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related quantitative field.
5+ years of combined experience in Data Architecture and hands-on Data Engineering roles.
Deep, hands-on experience with at least one major MDM platform (e.g., Informatica MDM, Boomi, Reltio, Semarchy, Talend MDM).
Strong background in designing and implementing A2A integrations using technologies like REST APIs, SOAP, Message Queues (e.g. Kafka, RabbitMQ), and Enterprise Service Buses (ESB).
Expert in Python or Scala.
Proficiency with SQL and relational/NoSQL databases.
Hands-on experience with cloud data platforms (AWS, Azure, or GCP).
Experience with ETL/ELT tools (e.g., Informatica PowerCenter, Talend, Fivetran, Data Build Tool - dbt).
Excellent analytical, problem-solving, and communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders.

Responsibilities

Define and govern the Master Data Management (MDM) strategy, roadmap, and architecture (e.g., Customer, Product, Vendor, etc.).
Design the conceptual, logical, and physical data models for MDM solutions, ensuring data lineage and governance are clearly defined.
Select and evaluate appropriate MDM technologies and tools.
Design scalable and robust Application-to-Application (A2A) integration architectures using various patterns (e.g., API, messaging, ETL/ELT).
Establish technical standards and best practices for data integration, security, and performance.
Collaborate with Data Governance teams to define data standards, policies, and quality rules.
Design systems for continuous data quality monitoring and remediation.
Provide technical leadership and mentorship to data engineering teams.
Drive adoption of modern data architecture principles (e.g., Data Mesh, Data Fabric).
Design, build, and maintain robust, scalable, and efficient ETL/ELT pipelines to ingest, transform, and load data from various source systems into the MDM hub and analytical platforms.
Develop and optimize data flows for real-time and batch A2A integrations.
Hands-on configuration, development, and testing of MDM platform components (e.g., matching rules, survivorship logic, data quality checks).
Develop custom connectors and services to synchronize master data across enterprise systems.
Implement data solutions using cloud-native services (e.g., AWS, Azure, GCP) including services like Kafka/Kinesis, Snowflake/Redshift/BigQuery, Spark/Databricks, and managed database services.
Write high-quality, maintainable code in languages like Python or Scala.
Implement CI/CD practices for data pipelines and infrastructure-as-code (IaC).
Set up comprehensive monitoring and alerting for data quality, pipeline performance, and health integration.