Introduction of ClearstateClearstate specializes in healthcare-focused market intelligence and strategic advisory services, assisting MedTech companies, biotechnology firms and healthcare service providers in identifying and leveraging market growth opportunities. Our data collection approach combines market research techniques, data processing and analytical frameworks.We are committed to delivering actionable strategies rooted in robust data analytics, built on the basis of market realities, empowering our clients to make informed decisions. Role: Data Analytics Engineer (Data Pipelines + Tableau) About the role:This is a hands-on role spanning two connected areas of our analytics workflow.
Upstream Foundation: Youll support ingesting data from external sources (APIs/files/web where appropriate), automating internal data pipelines (e.g., scheduled refreshes, dataset consolidation, basic data lineage tracking), and using ML-assisted approaches to map and clean data, while helping modernize our analytics foundation into a centralized data warehouse (e.g., BigQuery on Google Cloud).
Downstream Analytics Delivery: Youll support delivering analytics data products (e.g., reusable datasets and forecasting models) and translating results into trusted, interactive Tableau dashboards that stakeholders use for decision-making.
Key Responsibilities: Data ingestion, cleaning & automation
Ingest and integrate data from external/internal sources (APIs/files/spreadsheets/web where appropriate)
Build and maintain automated data workflows using Python and/or Google Apps Script (JavaScript)
Improve data quality and reduce manual work, including ML-assisted approaches to map and clean data (e.g., matching, deduplication, standardization)
Maintain and enhance existing scripts (Python/R) and convert ad-hoc work into repeatable processes
Data warehouse management
Build ETL/ELT workflows to load and transform data from external/internal sources into an online centralized data warehouse (e.g., BigQuery)
Maintain curated, analytics-ready datasets in the warehouse, including dimension/reference tables and mapping/crosswalk tables for standardized entities
Maintain analytics metadata such as data dictionary/metric definitions, refresh/run records, and data quality checks
Data products & analytics delivery
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Support development of reusable data products, including forecasting models where relevant
Build and maintain Tableau dashboards and reports; partner with stakeholders on requirements, metric definitions, and iteration
Communicate data assumptions/limitations clearly and help stakeholders use insights confidently
Key Qualifications:
Strong analytical mindset and comfort working with messy, real-world data
Working knowledge of SQL (joins, aggregations, building reliable queries)
Experience with data transformation using Python (or equivalent) through projects, internships, or prior roles
Experience building dashboards in Tableau (or similar BI tool) or strong interest in ramping up quickly
Ability to write maintainable work (clean logic, documentation) and validate outputs (basic checks/reconciliation)
Preferred / Nice-to-haves:
Experience building robust data ingestion pipelines (using APIs, or web scraping where appropriate) that handle challenges like authentication, pagination, and rate limits to convert raw data from diverse sources into analytics-ready datasets
Exposure to pipeline scheduling/automation (schedulers, or lightweight orchestration), monitoring, and data quality checks
Familiarity with Google Workspace workflows and Google Apps Script
Data modeling / warehouse fundamentals (facts/dimensions, curated marts, designing reliable datasets for reporting)
Google Cloud exposure, especially BigQuery; interest in moving analytics workflows to GCP over time
Experience with ML-assisted techniques for data mapping/cleaning (e.g., similarity scoring, fuzzy matching, entity resolution) and exposure to platforms like Vertex AI for enabling model training or inference workflows
AI-assisted coding development experience is welcome (not required). We value good judgment: reviewing, testing, and explaining what ships.
Why Clearstate
Hands-on opportunity to learn the full analytics lifecyclefrom raw data to automated workflows to dashboards used for real decisions
