Data Architect | US

Business data modeling

Trade-offs between different modeling philosophies – dimensional, 3NF, Data Vault

Conceptual vs physical modeling

Modeling techniques such as inheritance, parent / child tables, ragged structures, slowly changing dimensions etc.

Normalization vs de-normalization trade-offs

Detailed understanding the design trade-offs around different modeling approaches

Ability to lead model review sessions, and being able to lay out the design "options" and implications, and also present it in a way that both executives and technical-minded people can understand

Modern data delivery design patterns

Data as product

Design compromises & considerations

Know what exemplar deliverables look like

Team composition and responsibilities / work to be done

Streaming vs batch design patterns / considerations

Pros / cons of data mesh delivery model vs alternatives

Comparison of modern cloud native platforms vs legacy on-premises data solutions

Master data governance

Types and most common root cause of DQ issues

Remediation approaches

MDM architecture styles / patterns

Key capabilities of MDM & DQ vendors

Expert in technologies including 1 or more of each class:

Data management layer Snowflake Databricks Microsoft Fabric GCP Big Query/ AWS DB Options

Snowflake

Databricks

Microsoft Fabric

GCP Big Query/ AWS DB Options

Data acquisition & integration Azure Data Factory (ADF) Matillion FiveTran & HVR Keboola

Azure Data Factory (ADF)

Matillion

FiveTran & HVR

Keboola

Data transformation & orchestration ETL DBT Python / SQL Apache Airflow etc.

ETL

DBT

Python / SQL

Apache Airflow etc.

Vis: PowerBI Tableau Looker Domo / ThoughtSpot / Qlik / platform BI vendors (ORCL, SAP, AWS etc.)

PowerBI

Tableau

Looker

Domo / ThoughtSpot / Qlik / platform BI vendors (ORCL, SAP, AWS etc.)

Architecture transformations:

Considerations / experience evaluating lift & shift vs re-model trade-offs

Considerations / experience when consolidating decentralized silos

Considerations / experience when moving to modern cloud stacks

Considerations / experience when enabling unified operational & analytics data hubs

Communication & leadership skills:

Able to identify & evaluate most important criteria when making design decisions

Able to look around corners – recognize likely issues before they happen

Able to communicate complex subjects with executive leaders

Able to solicit input & feedback to model and design decisions

Ability to teach / leverage experience to develop team/talent around them

Use past experiences to help with change management to eases concerns over shifts in approach

Design the business data model based on the discovered business processes and data analysis

Translate business requirements into technical design specifications, including data streams, integrations, transformations, databases, and data warehouses.

Develop work estimates for Data Warehouse & Data Lake deliverables

Coach and mentor a team of a few dozen data engineers, analysts and ML Engineers on data architecture and modeling best practices

Define the data architecture framework, standards, and principles, including modeling, metadata, security, reference data such as product codes and client categories, and master data such as clients, vendors, materials, and employees

Define reference architecture, which is a pattern others can follow to create and improve data systems

Define data flows, i.e., which parts of the organization generate data, which require data to function, how data flows are managed, and how data changes in transition

Collaborate and coordinate with team members, clients and external SMEs

Bachelor’s degree in a technical or quantitative field (e.g. Computer Science, Math, Economics Statistics)

10+ years of work experience in the data analytics space

Previous experience in the consulting space is a plus

A passion for exploring and solving different kinds of problems

A desire to learn and assimilate technical information quickly

Hands-on experience deploying solutions in large-scale, high performing databases

Expertise aligned to technologies listed in Key Areas of Focus