Data Architect

A result- oriented thought-leader to drive the development of the data engineering practice

A trusted advisor and business partner to customers across verticals, and consulting team leader who establishes engineering processes and skill development.

Technology skills (any of below):

• Data engineering: analytical data platforms, streaming, big data, EDW, data lakes, data governance, data mesh, Spark, Kafka, Snowflake, (2 from below: AWS, GCP, Azure) Transactional databases: Redis, Cassandra, MongoDB, (2 from below: AWS, GCP, Azure) ML and MLOps: VertexAI, Sagemaker, Dataiku, Databricks, mlflow

Essential functions

Responsibilities:

Trusted Advisor:Provide direction to clients in different verticals on their selection of data and ML platforms, data strategies, expected impact, and relevant tools and methodologies that they can deploy to accelerate Big Data and advanced analytics programs.

Pre-Sales and Consulting:Lead and drive technology consulting engagements and pre-sales, architecture assessments, and discovery workshops. Plan data engineering programs of Fortune-1000 enterprises and lead a team of principal consultants and data engineers who work on client accounts and drive consulting engagements to success.

Technology Strategy and R&D:Create and bring to the market solutions in data engineering, MLOps, data governance. Responsible for driving innovation through research and development activities on industry trends, defining Go-To-Market strategies, developing

assets and solutions strategy across multiple industries.

Engineering:Working with Grid Dynamics's delivery organization to ensure that the right tools, technologies, and processes are in place across the firm to transform and continuously improve the quality and efficiency of our clients data platforms and data management processes.

Business Development & Partnership:

Manage relationships with key technology partners(AWS, GCP, Azure) and industry analysts.

Requirements:

• Extensive practical experience in Big Data engineering, data governance, and cloud data
• platforms.
• Strong understanding of cloud-based architectures for data collection, data aggregation, reporting, and BI.
• Strong understanding of ML platforms and tooling including open-source, cloud native, and proprietary.
• Deep domain knowledge in at least one of the following industries: Retail, Finance, Manufacturing, Healthcare, Life Sciences.
• Experience in managing and delivering sophisticated analytical and data engineering programs at the enterprise scale.
• Managed key client relations worldwide and advised global technology and business leaders on innovation and data strategy.
• Experience with Big 4 consulting is a plus.

Qualifications

Experience with enterprise Architecture.

Would be a plus

Enterprise Architecture