Sydney, AU · Open to relocation

Building data platforms that don't fall over at 2 a.m.

Senior Business Intelligence Engineer  Azure Data Engineer

4+ years architecting ETL/ELT pipelines, data models, and CI/CD-governed delivery on Microsoft Azure — Azure Data Factory, Databricks, Data Lake Storage, and Synapse — for teams that need the data to just be there, correctly, on time.

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Portrait of Abhishek Vadlamudi

01 — About

Engineering the platform, not just the report

I'm a Senior Business Intelligence Engineer based in Sydney with 4+ years designing, building, and optimising enterprise data platforms on Microsoft Azure. At Unisys, I've led end-to-end data engineering workstreams — from requirements through to production deployment — and made the architectural calls on pipeline design, data modelling, and integration patterns for a 10+ person engineering and analyst team.

My work sits across Azure Data Factory, Databricks, Azure Data Lake Storage, and Synapse Analytics on the engineering side, with SQL, Python, and PySpark doing the transformation heavy-lifting. On the delivery side, I've established CI/CD practices in Azure DevOps and built monitoring frameworks that catch failures before they become incidents — because a pipeline that fails silently is worse than one that fails loudly.

I also translate across the room: functional specs for engineers, support plans for operations, and plain answers for stakeholders who just want to know if the numbers are right. That combination — architecture depth plus the ability to explain it — is what I bring to a Data Engineer role.

4+ Years in data & BI engineering
20+ Azure Data Factory pipelines architected & maintained
10+ Person engineering/analyst team coordinated
~40% Reduction in manual reporting effort via automated refresh pipelines

02 — Skills

Stack & depth

Grouped by function, not buzzword density. Bar length reflects relative working depth, self-assessed.

Data Engineering & ETL

  • Pipeline Architecture (ADF)
  • ETL / ELT Design
  • Data Modelling & Warehousing
  • Azure Databricks / PySpark
  • REST API Integration

BI & Reporting

  • Power BI (DAX & Data Modelling)
  • Automated Refresh / Reporting Ops
  • SSRS
  • Tableau

Cloud & Azure

  • Azure Data Factory
  • Azure Data Lake Storage
  • Azure Synapse Analytics
  • Azure DevOps (CI/CD, Repos, Boards)

Languages & Tools

  • SQL (T-SQL, Stored Procs, Perf Tuning)
  • Python
  • PySpark
  • Git / Agile / Scrum

03 — Experience

Track record

  1. Senior Business Intelligence Engineer

    Feb 2022 — Present

    Unisys · Sydney, NSW

    • Led end-to-end data engineering workstreams from requirements through production deployment, owning solution design, testing, and handover within a 10+ person engineering/analyst team.
    • Made architectural decisions on pipeline design and data modelling patterns across Azure Data Factory, Databricks, and Azure Data Lake Storage.
    • Architected and maintained 20+ Azure Data Factory pipelines for ingestion, transformation, and integration across multiple source systems.
    • Designed SQL-based ETL workflows (stored procedures, views, scheduled jobs) modelling raw data into structured domain datasets consumed by Power BI and SSRS.
    • Established CI/CD pipelines and branching strategies in Azure DevOps, standardising deployment and reducing release risk across environments.
    • Integrated REST APIs as pipeline data sources within Azure Data Factory, ingesting external datasets into Azure Data Lake Storage.
    • Designed and implemented monitoring and alerting frameworks for critical pipelines, reducing undetected failures and improving mean time to resolution.
    • Automated Power BI dataset refresh pipelines, cutting manual reporting effort by ~40% and improving freshness from daily to near-real-time.
    • Built Python and PySpark processing scripts in Databricks notebooks for large-volume data cleansing, transformation, and enrichment.

  2. Commercial Banking Relationship Manager

    Apr 2019 — Jan 2020

    Axis Bank Ltd. · Telangana, India

    • Exceeded department KPIs by 20% for 5 consecutive months managing trade finance and FX payment workflows across import/export channels.
    • Translated complex financial information for diverse stakeholders — the same cross-audience communication now applied to data platform work.

04 — Projects

Selected engineering work

Framed by architecture and outcome, not by dashboard count.

Live pipeline · updates daily

AU Weather Medallion Pipeline — bronze → silver → gold

A self-built, production-pattern data pipeline for five Australian capital cities: Open-Meteo archive ingestion, an explicit data-quality gate (schema, nulls, range, duplicate, consistency checks), DuckDB SQL for rolling 30-day anomaly detection and 90th-percentile extreme-heat flagging, orchestrated on a real daily schedule via GitHub Actions with 15 passing pytest cases. Ships with Bicep IaC mapping the same design to production Azure — ADF, ADLS Gen2, Synapse Serverless SQL, Key Vault, Log Analytics alerting — deliberately not left running 24/7, because a standing cloud estate for a portfolio-scale workload is a cost decision, not a demo requirement.

Python · DuckDB · Plotly · pytest · GitHub Actions · Bicep (ADF / ADLS Gen2 / Synapse)

View on GitHub Live dashboard
Live pipeline · scheduled daily

AML Transaction Monitoring Pipeline — banking / financial services

A rule-based transaction-monitoring pipeline built for a banking-industry pivot: detects five real AML typologies — structuring, layering, impossible-travel, statistical amount outliers, high-risk-corridor routing — in SQL, validated with case-level recall tests against a synthetic ground truth (no real transaction data exists publicly, correctly). A naive mean/stddev outlier check initially caught only 20% of injected cases because the outlier was skewing its own baseline; switching to a robust median/MAD statistic took that to 100% — documented as a regression-guarded test, not just a fix. Bicep IaC is architected around what a bank's InfoSec review actually gates on: no public network access anywhere, immutable audit-grade storage, and Microsoft Purview for BCBS 239-aligned data governance.

Python · DuckDB (window functions, haversine SQL) · Plotly · pytest · GitHub Actions · Bicep (private endpoints, ADLS immutable storage, Purview, Key Vault RBAC)

View on GitHub Live dashboard
Azure Data FactoryDatabricksADLS

Multi-source ETL & pipeline architecture

Architected and maintain 20+ Azure Data Factory pipelines ingesting and transforming data from multiple source systems into Azure Data Lake Storage, with a SQL-based modelling layer (stored procedures, views, scheduled jobs) turning raw data into structured domain datasets for downstream Power BI and SSRS consumption.

Impact: reliable, trustworthy data foundation for all downstream analytics and reporting.

Azure DevOpsCI/CDGit

CI/CD standardisation for data engineering

Established CI/CD pipelines and branching strategies in Azure DevOps for data engineering artefacts, replacing ad-hoc deployment with a standardised process. Manage Repos and Boards to coordinate delivery and track work across the engineering lifecycle.

Impact: standardised deployment process, reduced release risk across environments.

ObservabilityIncident ResponseData Quality

Pipeline monitoring & alerting framework

Designed and implemented end-to-end monitoring and alerting for critical data pipelines — closing the gap between "pipeline failed" and "someone noticed," and giving the team a structured path to root cause.

Impact: reduced undetected failures, improved mean time to resolution for data incidents.

Power BIDAXAutomation

Power BI refresh automation

Automated Power BI dataset refresh pipelines and reporting data flows that were previously manual, rebuilding the delivery path from source to dashboard.

Impact: ~40% reduction in manual reporting effort; data freshness improved from daily to near-real-time.

05 — Education & Certifications

Foundations

Education

Master of Business Analytics Feb 2020 — Nov 2022

Deakin University, Burwood, VIC

  • Built sentiment analysis models on customer review data using Python.
  • Delivered descriptive analytics and visualisation solutions for multiple clients using Power BI, Tableau, Python, and Excel.
Post Graduate Diploma in Banking and Services Jun 2018 — Jun 2019

Manipal University, Karnataka, India

  • Distinction — GPA 8.97. Golden Key Society member (top 15% of cohort).
Bachelor of Computer Science and Engineering Sep 2014 — Jun 2018

Geetanjali College of Engineering and Technology, Telangana, India

  • Major: Software Engineering, Database Management Systems.

Certifications

Azure Fundamentals (AZ-900) Microsoft · 2021
Power BI Essential Training LinkedIn Learning · 2020

Currently deepening Azure data engineering depth (Data Factory, Databricks, Synapse) toward Azure Data Engineer Associate–level certification.

06 — Contact

Let's talk data platforms

Open to Azure Data Engineer / Senior Data Engineer roles. Based in Sydney, open to relocation.

Email vabhishek8@outlook.com Phone 0450 796 996 LinkedIn linkedin.com/in/abhishekvadlamudi
Location Sydney, NSW, Australia