Q

Quantiphi

AI-driven digital transformation and automation services

Data Architect

Data EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • More than 10 years of experience in technical, solutioning, and analytical roles.
  • At least 5 years of experience building and managing data lakes, data warehouses, data integration, data migration, and business intelligence or artificial intelligence solutions on cloud platforms such as Google Cloud Platform, Amazon Web Services, or Microsoft Azure.
  • Ability to understand business requirements, translate them into functional and non-functional areas, and define non-functional boundaries for availability, scalability, performance, security, and resilience.
  • Experience architecting, designing, and implementing end-to-end data pipelines and data integration solutions for varied structured and unstructured data sources and targets.
  • Experience with distributed computing and enterprise environments such as Hadoop and cloud platforms including Google Cloud Platform, Amazon Web Services, or Microsoft Azure.
  • Knowledge of cloud data integration and extract, transform, and load technologies such as Spark, PySpark, Scala, Dataflow, DataProc, and EMR.
  • Experience with traditional extract, transform, and load tools such as Informatica, DataStage, OWB, and Talend.
  • Deep knowledge of one or more cloud and on-premises databases such as Cloud SQL, Cloud Spanner, Bigtable, RDS, Aurora, DynamoDB, Oracle, Teradata, MySQL, DB2, and SQL Server.
  • Exposure to NoSQL databases such as MongoDB, CouchDB, Cassandra, and GraphDB.
  • Experience architecting and designing scalable cloud data warehouse solutions on BigQuery or Redshift.
  • Experience with data integration, storage, and data pipeline toolsets such as S3, Cloud Storage, Athena, Glue, Sqoop, Flume, Hive, Kafka, Pub/Sub, Kinesis, Dataflow, DataProc, Airflow, Composer, Spark SQL, Presto, and EMRFS.
  • Good understanding of cloud solutions for IaaS, PaaS, SaaS, containers, and microservices architecture and design.
  • Ability to compare products and tools across Google Cloud, AWS, and Azure technology stacks.
  • Good understanding of business intelligence reporting and dashboarding, and one or more associated tools such as Looker, Tableau, Power BI, SAP BO, Cognos, and Superset.
  • Understanding of security features and policies in one or more cloud environments such as Google Cloud Platform, AWS, or Azure.
  • Experience working on business transformation projects to move on-premises data solutions to cloud platforms such as Google Cloud Platform, AWS, or Azure.
  • Strong communication and articulation skills.
  • Good leadership skills.
  • Good analytical and problem-solving skills.

Responsibilities

  • Lead multiple data engagements on Google Cloud for data lakes, data engineering, data migration, data warehouses, and business intelligence.
  • Interface with multiple stakeholders in IT and business to understand data requirements.
  • Take responsibility for successful delivery of allocated projects according to schedule, quality, and customer satisfaction parameters.
  • Design and develop distributed, high-volume, multithreaded batch, real-time, and event-processing systems.
  • Implement processes and systems to validate data and monitor data quality, ensuring production data remains accurate and available for key stakeholders and dependent business processes.
  • Work with the presales team on RFPs and RFIs and help create data solutions.
  • Mentor developing talent within the team and define and track their growth parameters.
  • Contribute to building assets and accelerators.

Desired Qualifications

  • Preferred experience working with machine learning frameworks such as TensorFlow and PyTorch.

About the Company

Quantiphi

AI-driven digital transformation and automation services

Quantiphi uses AI and digital engineering to help organizations transform by making products and operations smarter and safer. It partners with clients to understand their challenges and then designs and builds customized AI and automation systems—combining machine learning, data analytics, and software engineering—delivering them under long-term contracts with ongoing support, and collaborating with Google Cloud to integrate technologies. Compared with competitors, it provides end-to-end, tailored solutions across industries rather than one-off projects, backed by long-term relationships and strong technology partnerships to scale. Its goal is to embed AI into products and processes to improve customer experiences and overall efficiency.

Company size
1,001-5,000
Company stage
Series A
Total funding
$23.4M
Headquarters
Marlborough, MA
Founded
2013

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