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ETL Lead

🗓️ Posted 2026-08-04 Melbourne, Victoria, Australia Full-time Hybrid ICT

Company shared salary

NA

Market rate

A$10,833–A$15,000/mo (A$129,996–A$180,000/yr)

Based on similar roles (title + domain + location).

About the Company

Strong SQL (Complex Joins, CTEs, Window Functions, Stored Procedures, Data Validation) Data Warehousing Concepts ETL Testing & Data Migration Testing Data Quality & Data Reconciliation Testing Unix/Linux Commands Hadoop/Hive/HDFS Cloud Data Validation (GCP/AWS) BI Reporting Validation (Power BI / Tableau / QlikView) Strong expertise in SQL (Joins, Subqueries, CTEs, Window Functions, Aggregations, Stored Procedures, Data Validation). Strong understanding of Data Warehousing concepts (Facts, Dimensions, SCD Type 1 & Type 2, Star Schema, Snowflake Schema). Hands-on experience in ETL Testing, Data Migration Testing, Data Reconciliation, and Data Quality Testing. Good understanding of Source-to-Target Mapping (STTM) validation. Experience with Unix/Linux commands and shell basics. Hands-on experience with Hadoop ecosystem (Hive, HDFS). Exposure to Cloud Data Platforms: GCP: BigQuery, GCS, AWS: S3, Athena, Redshift Knowledge of Teradata, Oracle, SQL Server, or similar enterprise databases. Experience validating Power BI, Tableau, or QlikView reports and dashboards. Experience with defect management tools such as JIRA and Agile methodologies.

Responsibilities

  • ● Perform ETL Testing, Data Warehouse Testing, Data Migration Testing, and Data Validation.
  • ● Validate Source-to-Target Mapping (STTM) documents and transformation rules.
  • ● Execute data reconciliation, completeness, accuracy, consistency, and integrity checks across multiple source and target systems.
  • ● Validate data pipelines across Hadoop, Hive, HDFS, GCP (BigQuery, GCS), and AWS (S3, Athena, Redshift).
  • ● Perform backend data validation using complex SQL queries.
  • ● Validate files and datasets across Unix/Linux, HDFS, GCS, S3, and relational databases.
  • ● Perform BI dashboard and report validation using Power BI, Tableau, or QlikView.
  • ● Execute Data Quality Testing, identify data anomalies, duplicates, missing records, and transformation issues.
  • ● Analyze defects, perform Root Cause Analysis (RCA), and coordinate with Data Engineering teams for resolution.
  • ● Participate in Agile ceremonies, sprint planning, backlog grooming, and release validation.