Data Technical Architect

As a Technical Data Architect candidate will:

• Technically Drive Data Automation Initiatives by Interacting with Customer Stakeholders:
• Architectural Vision: Define and champion the strategic architectural vision for end-to-end data automation across our diverse data pipelines, platforms, and consumption layers.
• Solution Design: Lead the design and implementation of highly scalable, efficient, and reliable automation frameworks for data ingestion, transformation, validation, and delivery processes.
• Stakeholder Engagement: Collaborate directly with internal and external customer stakeholders to understand complex business requirements, translate them into technical specifications, and ensure proposed automation solutions align with strategic objectives and deliver tangible business value.
• Technology Evaluation: Continuously evaluate, prototype, and recommend cutting-edge technologies, tools, and methodologies to advance our data automation capabilities and maintain a competitive edge.
• Mentor and Coach for Data Quality Engineering Solutioning:
• Technical Guidance: Serve as the principal technical mentor and coach for the Data Quality Engineering team, guiding them on architectural patterns, design principles, and best practices for data quality solutioning.
• Expertise Dissemination: Provide deep technical expertise in advanced data profiling, data validation, data cleansing, data reconciliation, anomaly detection, and comprehensive data governance strategies.
• Capability Building: Foster a culture of technical excellence, continuous learning, and shared ownership for data quality across engineering teams through hands-on guidance, code reviews, and knowledge-sharing sessions.
• Problem Solving: Lead complex technical deep-dives, architectural design reviews, and critical problem-solving sessions to ensure the robustness, scalability, and maintainability of data quality solutions.
• Drive Automation Efforts for Data Quality Test Automation Tool:
• Tool Strategy & Customization: Lead the architectural strategy, selection, customization, and integration of enterprise Data Quality Test Automation tools (e.g., Broadcom CA TDM, Informatica Data Quality, or custom frameworks).
• Automation Pipeline Design: Design and oversee the implementation of automated pipelines for data quality test case generation, execution, reporting, and integration with defect management systems.
• CI/CD Integration: Develop and implement robust strategies for embedding automated data quality gates directly into CI/CD pipelines, ensuring continuous data integrity throughout the entire software development lifecycle.
• Optimization: Ensure the optimal utilization and performance of the data quality test automation tool, maximizing its effectiveness and efficiency across various data domains.
• Support Interviews and Related Delivery by Leveraging Strong Big Data Understanding:
• Talent Acquisition: Play a critical role in the recruitment process for Data Quality Engineers and other Big Data-focused technical roles, conducting in-depth technical assessments.
• Technical Assessment: Evaluate candidates' proficiency in core Big Data technologies (e.g., Apache Spark, Hadoop, Kafka, data warehousing, data lakes, streaming platforms, cloud-native data services).
• Delivery Oversight: Provide overarching technical oversight and guidance on project delivery, ensuring architectural soundness, adherence to best practices, and resolution of complex technical impediments.
• Hands-on Knowledge and Experience in Advanced SQL, Python:
• Technical Prototyping & POCs: Lead by example with hands-on development, prototyping, and proof-of-concepts for complex data quality solutions and automation frameworks.
• Advanced SQL: Expert-level proficiency in writing and optimizing complex SQL queries for data analysis, validation, and manipulation across various database systems.
• Python Expertise: Deep hands-on experience with Python for data engineering, scripting automation, developing data quality checks, and integrating with Big Data frameworks (e.g., PySpark).

• Required Qualifications

• Bachelor s or master s degree in computer science, Engineering, or a related quantitative field.
• 15+ years of progressive experience in software engineering, with at least 5+ years in a Technical Architect, Lead Data Architect, or Principal Data Engineer role, specifically focused on data quality, data governance, or data platform architecture.
• Exceptional hands-on proficiency and deep architectural understanding of the Big Data ecosystem:
• Apache Spark (PySpark, Scala, or Java): Expert-level experience with Spark SQL, DataFrames/Datasets, streaming, and advanced performance tuning techniques.
• Distributed Storage & Processing: Hadoop, HDFS, S3, Delta Lake, Apache Iceberg, or similar data lake technologies.
• Streaming Technologies: Apache Kafka, AWS Kinesis, or similar high-throughput messaging systems.
• Cloud Data Platforms: Extensive experience designing and implementing solutions on AWS (e.g., EMR, Glue, Redshift, Lambda, Step Functions, S3), Azure (e.g., Databricks, Synapse Analytics, Data Lake Storage), or Google Cloud Platform (e.g., Dataproc, BigQuery, Cloud Storage).
• Expert-level hands-on experience with Advanced SQL for complex data analysis, validation, and optimization.
• Expert-level hands-on experience with Python for data engineering, automation, and developing robust data quality solutions.
• Proven track record of defining, designing, and implementing large-scale data automation frameworks.
• Demonstrated expertise in data quality engineering principles, methodologies, and tools (profiling, validation, cleansing, reconciliation, anomaly detection).
• Experience in leading and mentoring technical teams, fostering a culture of technical excellence and continuous improvement.
• Strong understanding of software development lifecycle (SDLC), DevOps practices, and integrating quality gates into CI/CD pipelines.
• Excellent communication, presentation, and interpersonal skills, with the ability to articulate complex technical concepts to diverse audiences, including senior leadership and non-technical stakeholders