Data Analyst
Listed on 2026-09-04
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IT/Tech
Data Engineering, Data Analyst, Data Warehousing, Business Intelligence
Required skills & Must have experience:
Data Analysis & Querying:
Expert-level SQL with strong hands‑on experience in Big Query and/or PostgreSQL, including complex queries, window functions, joins, aggregations, query optimization, and dataset profiling.
Strong Python experience for data analysis, exploration, automation, and validation.
Ability to independently perform exploratory data analysis, identify anomalies, trends, data inconsistencies, and perform root‑cause analysis.
Business Intelligence & Insight Generation:Visualization & BI:
Strong hands‑on experience with Power BI or equivalent enterprise BI platform.
Dashboard Design:
Ability to create meaningful dashboards focused on decision‑making purpose.
Experience defining and standardizing business metrics, KPIs, and calculation logic.
Data Modeling & Analytics:Strong understanding of star schema, dimensional modeling, fact and dimension tables, normalization, denormalization, and analytical data models.
Understanding of the journey from raw data to curated, analytics‑ready datasets.
Data Governance & Trust:- Hands‑on experience with a data catalog platform such as Data Hub, or GCP Dataplex, including dataset onboarding, discovery, metadata management, and ownership.
- Experience managing technical and business metadata, including dataset descriptions, ownership and data dictionary.
- Experience using automated data lineage (Open Lineage, Marquez, or Big Query‑native lineage) to perform impact analysis and investigate data‑quality issues.
- Experience defining and validating data quality rules covering completeness, freshness, uniqueness, validity, consistency, and referential integrity.
- Understanding of data classification, including PII and sensitive data handling requirements.
- Working knowledge of data access governance, IAM, and role‑based access concepts.
Strong experience with Google Cloud Platform, particularly Big Query and GCS.
Understanding of data ingestion, transformation, storage, and consumption patterns.
Working knowledge of orchestration platforms such as Apache Airflow, Astronomer, or Cloud Composer, including dependencies, scheduling, failure handling, and data freshness.
Strong understanding of how analytical datasets are produced and consumed across an enterprise data platform.
Nice to have:- dbt for transformation documentation and lineage-as-code
- Experience with a data privacy or compliance framework such as GDPR or CCPA.
- Experience with data observability or monitoring capabilities.
- Exposure to statistical analysis, forecasting, or advanced analytics.
- Experience with AI-assisted analytics.
- Automotive, connected vehicle, vehicle telemetry, or high-volume event data domain experience.
- Experience working with streaming or near‑real‑time datasets.
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