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.
Advanced SQL skills – able to write complex, performant, and maintainable queries across large datasets.
Strong Python proficiency – comfortable building data transformation scripts, pipeline logic, custom Airflow operators.
Requirements:
Architecture-first thinking — assess if existing solutions can be extended before coding.
Efficiency over volume — focus on minimizing the number of tables or pipelines needed.
End-to-end ownership — manage data from ingestion to Tableau dashboards.
Pragmatic scalability — design systems that can support future projects without extensive rework.
Job description
Our client, a Banking company, is looking for a Senior Data Engineer & Analytics Developer for their Remote location.
Responsibilities:
Architecture-first thinking — Before writing a single line of code, they ask: "Does this already exist? Can I extend what's here? Will this serve more than just today's ask?"
Efficiency over volume — Measures success not by how many tables or pipelines they create, but by how few they need to support a growing number of use cases.
End-to-end ownership — Comfortable moving from raw ingestion all the way through to a polished Tableau dashboard, understanding how each layer impacts the next.
Pragmatic scalability — Designs for the future without over-engineering for the present; builds foundations that can absorb new projects without architectural rework.
Requirements:
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.
