ICONMA is a client-focused company seeking a Senior Data Engineer & Analytics Developer for a remote position. The role involves designing scalable data architectures, building data pipelines, and developing Tableau dashboards to support business analytics.
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
- 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
- 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
- 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
- 5+ 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
- Terraform