Data Analysis

Data Analysis & Querying:

• Expert-level SQL with strong hands-on experience in BigQuery 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 DataHub, 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 (OpenLineage, Marquez, or BigQuery-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.

Data Platform & Technical Ecosystem:

• Strong experience with Google Cloud Platform, particularly BigQuery 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.