Senior Data Engineer & Analytics Developer - Remote - Banking - W2

Core Competencies Data Engineering and Pipeline Development

Deep, hands-on experience with Google BigQuery — including dataset design, partitioning/clustering strategies, materialized views, and cost-optimization techniques.

Proficiency in Cloud Composer (Apache Airflow) for orchestrating complex, production-grade data pipelines with proper scheduling, retry logic, and dependency management.

Experience building and maintaining Vertex AI Pipelines for ML workflows and data transformation at scale.

Advanced SQL skills — able to write complex, performant, and maintainable queries across large datasets including window functions, CTEs, recursive queries, and query optimization.

Strong Python proficiency — comfortable building data transformation scripts, pipeline logic, custom Airflow operators, API integrations, and automation tooling.

Data Architecture and Scalable Design

Proven ability to design layered data architectures using patterns such as Medallion (bronze/silver/gold), Dimensional Modeling (star schema), Data Vault, and targeted denormalization — and knows when to apply each based on the use case.

Track record of building modular, multi-purpose datasets rather than project-specific tables — thinks in terms of canonical models and shared dimensions.

Understands when to create new tables versus when to extend, view, or restructure existing assets to avoid unnecessary duplication and table sprawl.

Applies best practices around naming conventions, schema organization, documentation, and lifecycle management so that the architecture remains navigable as it scales.

Tableau Dashboard Development

Hands-on experience building production-quality Tableau dashboards — from data source configuration and extract optimization to interactive visual design.

Ability to translate business questions into clear, intuitive visualizations that non-technical stakeholders can self-serve from.

Familiarity with Tableau performance tuning, published data sources, and server/cloud publishing workflows.

Understands the relationship between upstream data modeling decisions and downstream dashboard performance — designs the data layer with the visualization in mind.

Technical Stack

Cloud Platform: Google Cloud Platform (GCP)

Data Warehouse: BigQuery (advanced)

Orchestration: Cloud Composer / Apache Airflow

ML Pipelines: Vertex AI Pipelines

Visualization: Tableau (Desktop, Server/Cloud)

Languages: Python (advanced), SQL (advanced)

Infrastructure: Terraform (preferred), GCS, Cloud Functions

Version Control: Git / GitLab

Experience and Qualification

s5+ years in a data engineering role, with meaningful GCP/BigQuery experience

.Advanced proficiency in Python and SQL as daily working languages

.Demonstrated experience designing and maintaining shared, reusable data models in an enterprise or multi-team environment

.Familiarity with data architecture patterns including Medallion, star schema, and Data Vault

.Portfolio or examples of Tableau dashboards built on well-structured data layers

.Familiarity with CI/CD practices for data pipelines and infrastructure-as-code concepts

.Strong communicator who can work with cross-functional teams to gather requirements and translate them into scalable data solutions

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