Data Engineer & Data Analyst

Use joins, subqueries, window functions, CTEs and query optimisation.

Apply descriptive statistics, probability, distributions, hypothesis testing and regression.

Clean and process data with Python and Pandas, including missing values and outliers.

Work with OLTP, OLAP, normalisation, star and snowflake schemas, Data Vault and data grain.

Use dimensions, measures, cubes, drill-down, roll-up, slice, dice and pivot analysis.

Build clear interactive dashboards with Power BI or Tableau and well-chosen metrics.

Run A/B tests and analyse statistical power, funnels, cohorts and retention.

Turn analysis into clear narratives, executive summaries and actionable recommendations.

Design batch and streaming pipelines with idempotency, retries, backfills and change data capture.

Build DAGs, tasks, schedules, dependencies, retries, alerts and SLA monitoring.

Create warehouse transformations, slowly changing dimensions, tests, documentation and lineage.

Use Fivetran or Airbyte with Snowflake, BigQuery or Redshift, cloud storage and lakehouse patterns.

Process distributed data with Spark or PySpark and near-real-time events with Kafka.

Validate data and improve pipeline reliability with dbt tests, CI/CD and GitHub Actions.

Apply lineage, governance, PII masking, privacy and AI-ready data practices.

Deliver an end-to-end cloud pipeline, OLAP analysis, A/B test, BI dashboard and stakeholder presentation.