Data Analytics Support Specialist

Overview

We are looking for a Data Analytics Support Specialist who bridges the gap between raw data and business decisions. This role is equal parts analyst, visual storyteller, and platform engineer. You will support analytics workflows end-to-end — from ingestion and transformation to dashboard delivery and model operationalization — with the flexibility to contribute across MLOps and DevOps pipelines when needed.

The ideal candidate thinks in systems, communicates through visuals, and is comfortable toggling between a BI tool and a CI/CD pipeline in the same afternoon.

Responsibilities

Analytics & Workflow Support

• Understand, document, and improve existing analytics workflows across data ingestion, transformation, and reporting layers.
• Collaborate with data engineers and business stakeholders to map end-to-end data pipelines and identify bottlenecks or gaps.
• Maintain and optimize SQL queries, dbt models, and ETL/ELT processes to ensure data reliability and freshness.
• Triage and resolve analytics issues across production dashboards, reports, and ad hoc requests.

Visual & Dashboard Development

• Design and build clear, compelling data visualizations using tools such as Tableau, Power BI, Looker, or equivalent.
• Translate complex analytical findings into concise visual narratives for technical and non-technical audiences.
• Establish and enforce standards for chart selection, color usage, layout, and data labeling across reporting assets.
• Create process-flow diagrams, data lineage maps, and architecture visuals for internal documentation and stakeholder communication.

MLOps & Model Support

• Assist in the deployment, monitoring, and versioning of machine learning models in production environments.
• Support feature engineering pipelines and model retraining schedules using platforms such as MLflow, SageMaker, Vertex AI, or similar.
• Maintain model performance dashboards and alert on drift, degradation, or data quality issues upstream of model inputs.
• Collaborate with data scientists to package and containerize models for reliable deployment.

DevOps & Infrastructure Flexibility

• Contribute to CI/CD pipeline configuration for data and ML workflows (GitHub Actions, Jenkins, GitLab CI, or equivalent).
• Support containerized workloads using Docker and Kubernetes in data platform contexts.
• Assist in managing cloud-based data infrastructure across AWS, GCP, or Azure — including cost monitoring and environment configuration.

Apply infrastructure-as-code principles (Terraform, Pulumi) where applicable to analytics environments.

Qualifications

• 3+ years of experience in a data analytics, data engineering, or analytics engineering role.
• Strong proficiency in SQL; experience with Python (pandas, NumPy, or equivalent) for data manipulation.
• Demonstrated ability to understand and document complex multi-step analytics workflows.
• Hands-on experience building and publishing dashboards in Tableau, Power BI, Looker, or a comparable BI platform.
• Familiarity with data pipeline orchestration tools (Airflow, Prefect, Dagster, or similar).
• Working knowledge of at least one cloud data warehouse (Snowflake, BigQuery, Redshift, Databricks).
• Exposure to MLOps concepts — model registry, experiment tracking, deployment, monitoring.
• Comfort working in version-controlled environments (Git) and adhering to software development best practices.

Preferred Qualifications

• Experience with dbt for data transformation and documentation.
• Hands-on use of MLflow, SageMaker, Vertex AI, or Azure ML for model lifecycle management.
• Familiarity with containerization (Docker) and orchestration (Kubernetes) in data contexts.
• Experience with CI/CD tooling (GitHub Actions, GitLab CI, Jenkins) for data or ML pipelines.
• Exposure to infrastructure-as-code tools such as Terraform or Pulumi.
• Background in statistical analysis, A/B testing, or experimentation frameworks.
• Experience producing data lineage documentation or working with data catalog tools (e.g., Atlan, DataHub, Alation).
• Experience with Quickbase for low-code application development, workflow automation, and custom reporting on operational data — especially in support of business users who self-serve outside of core BI tooling.
• Proficiency in Alteryx for self-service data blending, preparation, and workflow automation — including experience building repeatable Designer workflows for analytics and reporting pipelines.

Consilio, LLC is an EEO/Affirmative Action Employer and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other legally protected status.