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Job Title: Data Architect / Delivery Lead Job Summary: The Data Architect / Delivery Lead will provide technical expertise in the analysis, design, development, rollout, and maintenance of enterprise data models and solutions, utilizing both traditional and emerging technologies such as cloud, Hadoop, NoSQL, and real-time data processing. In addition to technical expertise, the role requires leadership in driving cross-functional teams, ensuring seamless project delivery, and fostering innovation within the team. The candidate must excel in managing data architecture projects while mentoring teams in data engineering practices, including PySpark , automation, and big data integration. Essential Duties Data Architecture Design and Development: Design and develop conceptual, logical, and physical data models for enterprise-scale data lakes and data warehouse solutions, ensuring optimal performance and scalability. Implement real-time and batch data integration solutions using modern tools and technologies such as PySpark, Hadoop, and cloud-based solutions (e.g., AWS, Azure, Google Cloud). Utilize PySpark for distributed data processing, transforming and analyzing large datasets for improved data-driven decision-making. Understand and apply modern data architecture philosophies such as Data Vault, Dimensional Modeling, and Data Lake design for building scalable and sustainable data solutions. Leadership & Delivery Management: Provide leadership in data architecture and engineering projects, ensuring the integration of modern technologies and best practices in data management and transformation. Act as a trusted advisor, collaborating with business users, technical staff, and project managers to define requirements and deliver high-quality data solutions. Lead and mentor a team of data engineers, ensuring the effective application of PySpark for data engineering tasks, and supporting continuous learning and improvement within the team. Manage end-to-end delivery of data projects, including defining timelines, managing resources, and ensuring timely, high-quality delivery while adhering to project methodologies (e.g., Agile, Scrum). Data Movement & Integration: Provide expertise in data integration processes, including batch and real-time data processing using tools such as PySpark, Informatica PowerCenter, SSIS, MuleSoft, and DataStage. Develop and optimize ETL/ELT pipelines, utilizing PySpark for efficient data processing and transformation at scale, particularly for big data environments (e.g., Hadoop ecosystems). Oversee data migration efforts, ensuring high-quality and consistent data delivery while managing data transformation and cleansing processes. Documentation & Communication: Create comprehensive functional and technical documentation, including data integration architecture documentation, data models, data dictionaries, and testing plans. Collaborate with business stakeholders and technical teams to ensure alignment and provide technical guidance on data-related decisions. Prepare and present technical content and architectural decisions to senior management, ensuring clear communication of complex data concepts. Skills and Experience: Data Engineering Skills: Extensive experience in PySpark for large-scale data processing, data transformation, and working with distributed systems. Proficient in modern data processing frameworks and technologies, including Hadoop, Spark, and Flink. Expertise in cloud-based data engineering technologies and platforms such as AWS Glue, Azure Data Factory, or Google Cloud Dataflow. Strong experience with data pipelines, ETL/ELT frameworks, and automation techniques using tools like Airflow, Apache NiFi, or dbt. Expertise in working with big data technologies and frameworks for both structured and unstructured data. Data Architecture and Modeling: 5-10 years of experience in enterprise data modeling, including hands-on experience with ERwin, ER/Studio, PowerDesigner, or similar tools. Strong knowledge of relational databases (e.g., Oracle, SQL Server, Teradata) and NoSQL technologies (e.g., MongoDB, Cassandra). In-depth understanding of data warehousing and data integration best practices, including dimensional modeling and working with OLTP systems and OLAP cubes. Experience with real-time data architectures and cloud-based data lakes, leveraging AWS, Azure, or Google Cloud platforms. Leadership & Delivery Skills: 3-5 years of management experience leading teams of data engineers and architects, ensuring alignment of team goals with organizational objectives. Strong leadership qualities such as innovation, critical thinking, communication, time management, and the ability to collaborate effectively across teams and stakeholders. Proven ability to act as a delivery lead for data projects, driving projects from concept to completion while managing resources, timelines, and deliverables. Ability to mentor and coach team members in both technical and professional growth, fostering a culture of knowledge sharing and continuous improvement. Other Essential Skills: Strong knowledge of SQL, PL/SQL, and proficiency in scripting for data engineering tasks. Ability to translate business requirements into technical solutions, ensuring that the data solutions support business strategies and objectives. Hands-on experience with metadata management, data governance, and master data management (MDM) principles. Familiarity with modern agile methodologies, such as Scrum or Kanban, to ensure iterative and successful project delivery. Preferred Skills & Experience: Cloud Technologies: Experience with cloud data platforms such as AWS Redshift, Google BigQuery, or Azure Synapse for building scalable data solutions. Leadership: Demonstrated ability to build and lead cross-functional teams, drive innovation, and solve complex data problems. Business Consulting: Consulting experience working with clients to deliver tailored data solutions, providing expert guidance on data architecture and data management practices. Data Profiling and Analysis: Hands-on experience with data profiling tools and techniques to assess and improve the quality of enterprise data. Real-Time Data Processing: Experience in real-time data integration and streaming technologies, such as Kafka and Kinesis. Show more Show less
