Description
data warehousing
architecture runway
technical oversight
snowflake cloud
data pipelines
data governance
The Finance Analytics Architect will partner with finance analytics product owners to shape data product architectures that meet corporate finance needs. This role blends deep technical expertise with functional finance knowledge, requiring familiarity with corporate finance processes. The architect will design secure, scalable, cost-effective solutions, lead technical discussions, and mentor junior engineers, especially on Snowflake and cloud platforms.
Key responsibilities include collaborating with product owners and project managers to align data mesh strategies, defining stakeholder scopes, schedules, and budgets. The architect will lead the design of data products, build enterprise‑grade datasets, and deliver robust pipelines for extraction, transformation, and loading while maintaining comprehensive documentation and data lineage. They will evaluate technical capabilities across value streams, execute proofs of concept, and ensure compliance with enterprise guardrails. Supporting continuous integration and delivery, they will also implement governance frameworks to uphold data quality, privacy, and security.
- Architecting data warehouses for at least one functional domain such as finance or supply chain
- Planning and developing architectural runway to support business outcomes
- Providing technical oversight and promoting security, quality, and automation
Requirements
snowflake
python
sql
azure
power bi
be degree
Candidates must hold a BE or equivalent degree and bring over ten years of experience in data engineering and analytics. The role demands proven ability to construct data warehouses, lakes, or lakehouses, and to model finance‑related data such as accounts payable, receivable, and general ledger. Proficiency with Snowflake, advanced SQL, Python, dimensional modeling, and cloud platforms like Azure is essential, as is experience with data security, governance, and CI/CD practices. Strong analytical, communication, and leadership skills are required to drive business value and mentor teams.
- Experience in building data warehouses, data lakes, or lake houses
- Knowledge of finance data concepts including accounts payable, receivable, general ledger, and financial close
- Familiarity with Snowflake features and functionality
- Expertise in complex SQL, Python scripting, and performance tuning
- Understanding of Snowflake data engineering practices and dimensional modeling to optimize performance and scalability
- Experience with data security and access controls in Snowflake
- Ability to set up security frameworks, governance, and compliance measures such as SOX
- Advanced SQL skills for building monitors and resource monitors in Snowflake
- Excellent analysis and documentation skills, including prototyping
- Ability to analyze complex business processes and map them to data requirements
- Extensive experience applying best practices in data engineering and visualization
- Advanced experience creating interactive analytics solutions using Power BI and Python
- Extensive experience with Azure cloud platforms for data storage and processing
- Expertise in dimensional and transactional data modeling across OLTP, OLAP, NoSQL, and big data technologies; familiarity with platforms like Cloudera, Databricks, Dataiku, Snowflake, dbt, Coalesce, and data mesh
- Experience developing and supporting data pipelines with code, orchestration, quality, and observability
- Expert-level programming ability in Python, Spark, SQL, and PL/SQL; intermediate experience with batch, on‑demand, and streaming integration methods
- Intermediate experience with DevOps, CI/CD principles, and tools; proficiency in Azure Data Factory
- Experience with data governance frameworks to ensure data quality, privacy, and compliance; solid understanding of cybersecurity concepts such as encryption and hashing
- Strong analytical skills to evaluate, reconcile, and abstract data from multiple sources
- Awareness of emerging technologies relevant to the organization
- Recognized as a key enterprise‑level data leader
- Wearing a techno‑functional hat to support the team as needed
Benefits
Information not given or found
Training + Development
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