Consultant – Insights Data & Reporting

Role overview

Data engineering

• Build and manage ADF pipelines with lookups, copy activities, parameters, scheduled triggers, and failure handling.
• Extract from Oracle, DB2/iSeries, MySQL, and SQL Server using incremental loading; land data in ADLS as Parquet and load Azure Synapse with stored procedures, staging, and MERGE/upsert patterns.
• Implement dimensional loads in incremental, staging, dimensions, and facts sequence while maintaining data grain; handle Snowflake loads such as Snowpipe where applicable.
• Work with Databricks/PySpark when required; use SQL/T-SQL window functions, CTEs, stored procedures, and performance tuning.
• Reconcile data using counts, keys, and totals; implement idempotent reruns, late-data replay, and end-to-end schema drift handling.
• Manage Git CI/CD across Dev, QA, Beta/Test, and Prod for ADF and Synapse scripts; perform post-deployment validation.

Power BI reporting

• Deliver Power BI semantic modeling, DAX, RLS, and Import, DirectQuery, Dual, and Direct Lake modes; configure incremental refresh.
• Build paginated RDL reports with Tablix, page breaks, parameters, and model or SQL binding.
• Manage Power BI capacity, Fabric/Power BI deployment pipelines, environment rules, and Git integration.
• Optimize model and report performance and triage production issues across sources, models, reports, RLS, and refresh.

Must-have qualifications

• 6–8 years across data engineering and Power BI, with production ownership in both areas.
• Strong ADF and Azure Synapse/SQL Server warehouse experience and hands-on ADLS/Parquet landing patterns.
• Strong dimensional modeling, reconciliation, and idempotent data-load knowledge.
• Strong Power BI modeling, DAX, RLS, and paginated report skills.
• Experience with CI/CD and environment promotion for both data engineering and BI artifacts.
• Strong production troubleshooting and issue-resolution capabilities.

Good to have

• Snowflake; Databricks, PySpark, or Delta; Microsoft Fabric; Azure DevOps or GitHub.
• Experience supporting production environments in a ticket-driven environment.

Schedule

• The stated shift is 3:00 PM–11:00 PM IST.