Title- Data Architect
Location: either New York city or Lake Mary, FL
Onsite: twice a week
Contract: 6-12 months to perm
Interview process: two video interviews to hire (they might want onsite for final)
Must have: data architect who has most recent financial experience, hands on experience with data modeling, canonical data models Erwin, data governance
- Modern cloud data platforms as well (Azure and Snowflake would be the priority, but also AWS, GCP, Databricks, etc.)
- Real-time data streaming: look out for Kafka & Spark streaming would be the priority, but can also look for Apache Pulsar, AWS Kinesis, Azure Event Hubs, Google Pub/Sub, or more generally 'streaming' keywords.
- n Architect who's also still comfortable with hands-on delivery
- Background in Financial Services and deep knowledge of financial data
BNY Mellon Data Modeler Project: Overview: Part of a larger initiative around strategizing the data Architecture of the functional areas of Alternatives clients and overall horizontal data delivery strategy for fund services (accounting/administration). Have incremental funding and strategic imperative to build out offering to clients from data perspective, how they interface through client channels to give access to client data and build a larger data product offering.
They have seen a lot of growth in alternatives business driving the need for a better data organization and reporting strategy, taking more nonstandard/nontraditional views of data from accounting perspective and integrate it with overall way they look at data models for clients across assets and servicing space.
Time to market is critical here and they are lacking the internal expertise and capacity to deliver at the velocity needed.
Data Architect: - Preferable to have domain expertise in Alternatives or accounting in general to understand the data structures, language, how data is typically labeled and organized in Alternatives systems.
- Data sources include their Alternatives/Accounting platforms, NoSQL MongoDB, some Relational, largely structured data but some more loosely structured in NoSQL, and ingesting data into a Snowflake data warehouse.
- Building canonical data models that will be applied across the board as anyone is trying to publish data
- Canonical models are not defined yet the Architect will play the primary role in strategizing here.
- Client/stakeholder facing aspect here to understand the data sources, how the data is structured, and be able to break down to entity and attributes, and build conceptual models
- Building a data ingestion layer, operational data stores, data distribution layer and corralling data sources to conform to canonical data models
- The architect should have depth of expertise in data modeling, but broader experience and knowledge of data architecture, ETL/ingestion processes, data governance, etc.
- Kafka and Spark nice to have let's prioritize this now.