Lead Data Architect

Role Overview

• Define and implement a scalable, enterprise-wide data architecture aligned with business and technology goals.
• Develop a data strategy roadmap, ensuring long-term sustainability, scalability, and efficiency.
• Partner with executive leadership, product teams, and engineering to ensure data initiatives drive business value.
• Establish enterprise data governance, security, and compliance frameworks leveraging tools like Collibra or Alation.
• Oversee the design and evolution of data lakes, data warehouses, and cloud-based analytics platforms using Databricks, Snowflake, BigQuery, or Redshift.
• Lead the adoption of modern data architecture patterns, including event-driven architectures, real-time data streaming (Kafka, Pulsar), and AI-driven analytics.
• Provide guidance on database optimization, indexing, partitioning, and storage strategies for tools like PostgreSQL, MySQL, and NoSQL solutions like MongoDB or Cassandra.
• Evaluate emerging technologies, making recommendations for tools and platforms that enhance data capabilities.
• Direct ETL/ELT strategies, ensuring seamless data flow across systems with Python, Apache Airflow, dbt, or Informatica.
• Architect cloud-based solutions (AWS, Azure, or GCP) using services such as AWS Glue, Azure Synapse, and Google Cloud Dataflow to support analytics, AI, and operational use cases.
• Ensure API-first design for data integration using GraphQL, RESTful APIs, or event-driven architectures (Kafka, AWS Kinesis, Pub/Sub).
• Define and oversee data quality, lineage, and cataloging efforts using Great Expectations, Monte Carlo, or DataHub.
• Develop policies for data privacy, access control, and encryption, ensuring compliance with GDPR, CCPA, HIPAA, or other relevant regulations.
• Implement enterprise-wide metadata management and data lineage tracking using Collibra, Alation, or Data Catalog solutions.
• Drive best practices for data security and compliance audits, leveraging IAM tools and cloud security solutions.
• Lead a team of data architects, engineers, and analysts, mentoring them on best practices.
• Act as a liaison between business and technical teams, translating business needs into scalable data solutions.
• Champion a culture of innovation, ensuring the data team is adopting cutting-edge methodologies.

Requirements

• 10+ years of experience in data architecture, data engineering, or related fields.
• Bachelor’s degree (Master’s preferred) in Computer Science, Applied Mathematics, Statistics, Machine Learning, or a closely related field (or foreign equivalent).
• Proven track record in designing large-scale, enterprise data architectures.
• Expertise in SQL, NoSQL, and distributed database technologies such as Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, MongoDB, and Cassandra.
• Strong experience with cloud-based data platforms (AWS, Azure, GCP) and services like AWS Glue, Azure Data Factory, and Google Dataflow.
• Deep understanding of data modeling, ETL/ELT processes, and data pipeline optimization using dbt, Apache Airflow, Informatica, or Talend.
• Experience with real-time streaming technologies (Kafka, Spark Streaming, Apache Flink, AWS Kinesis).
• Strong knowledge of data security, governance, and compliance frameworks.
• Excellent verbal and written communication skills and ability to resolve disputes effectively and efficiently
• Outstanding presentation and public speaking skills
• Mastery independent decision making, analysis and problem-solving skills
• Ability to quickly understand and assess complex projects, systems and ecosystems and identify relevant relationships and connections between them
• Mastery planning and organizational skills and techniques
• Communicate effectively with senior management and key stakeholders
• Ability to influence, build relationships, understand organizational complexities, manage conflict and navigate politics
• Familiarity with the healthcare data domain with previous experience working with healthcare datasets is a plus
• Strong Python programming skills, with expertise in data manipulation and pipeline development using Pandas, PySpark, NumPy, and SQLAlchemy.
• Experience with AI/ML-driven analytics architectures and MLOps frameworks like MLflow or SageMaker.
• Hands-on experience with Infrastructure as Code (Terraform, CloudFormation).
• Familiarity with Graph databases and knowledge graphs (Neo4j, Amazon Neptune).
• Certifications in cloud data services (AWS Certified Data Analytics, Google Professional Data Engineer, Databricks Certified Data Engineer).

Tech Stack

• Airflow
• Amazon Redshift
• Apache
• AWS
• Azure
• BigQuery
• Cassandra
• Cloud
• ETL
• Google Cloud Platform
• GraphQL
• Informatica
• Kafka
• MongoDB
• MySQL
• Neo4j
• NoSQL
• Numpy
• Pandas
• Postgres
• Pulsar
• PySpark
• Python
• Spark
• SQL
• Terraform

Benefits

• Medical, Dental and Vision Coverage
• 401K Plan with Company Match
• PTO
• Paid Parental Leave
• Income Protection
• Work Life Assistance Program
• Flexible Spending Accounts
• Educational Benefits
• Worldwide Scholarship Program
• Volunteer Opportunities