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Muhammad H.

Karachi, Pakistan

$10/hr
5.0
1 jobs

Slow pipelines, unreliable data, or a warehouse that breaks every time the source changes? I build data systems that don't. I'm a ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—™๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ ๐——๐—ฎ๐˜๐—ฎ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ with 5 years of experience delivering end-to-end data engineering and BI solutions. I currently work at Pakistan's largest payment gateway, a high volume fintech environment where ๐—ง๐—•-๐˜€๐—ฐ๐—ฎ๐—น๐—ฒ ๐˜๐—ฟ๐—ฎ๐—ป๐˜€๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—ฑ๐—ฎ๐˜๐—ฎ, strict governance, and zero tolerance for pipeline failures are the daily reality. My specialty is building systems that are ๐—ฎ๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐—ฒ๐—ฑ ๐—ฝ๐—ฟ๐—ผ๐—ฝ๐—ฒ๐—ฟ๐—น๐˜† ๐—ณ๐—ฟ๐—ผ๐—บ ๐˜๐—ต๐—ฒ ๐˜€๐˜๐—ฎ๐—ฟ๐˜ metadata-driven, layered, monitored, and built to scale. ๐—ช๐—›๐—”๐—ง ๐—œ ๐—•๐—จ๐—œ๐—Ÿ๐—— โœฆ ๐— ๐—ฒ๐˜๐—ฎ๐—ฑ๐—ฎ๐˜๐—ฎ-๐——๐—ฟ๐—ถ๐˜ƒ๐—ฒ๐—ป ๐—˜๐—ง๐—Ÿ/๐—˜๐—Ÿ๐—ง ๐—ฃ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ๐˜€ Control logic lives in configuration, not hardcoded. One framework handles dozens of sources with built-in logging, error handling, and restartability. Proven: reduced ETL runtime by ๐Ÿฏ๐Ÿด% on a production enterprise warehouse by eliminating redundant mapping layers. โœฆ ๐—˜๐—ป๐˜๐—ฒ๐—ฟ๐—ฝ๐—ฟ๐—ถ๐˜€๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—ช๐—ฎ๐—ฟ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ฒ ๐——๐—ฒ๐˜€๐—ถ๐—ด๐—ป End-to-end warehouse design across ๐—ฆ๐˜๐—ฎ๐—ด๐—ถ๐—ป๐—ด โ†’ ๐—–๐—ผ๐—ฟ๐—ฒ โ†’ ๐—š๐—ผ๐—น๐—ฑ (Medallion Architecture), with star/snowflake schema modeling, incremental loading, duplicate handling, and structured audit logging baked in. โœฆ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—™๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ ๐—ฆ๐—ผ๐—น๐˜‚๐˜๐—ถ๐—ผ๐—ป๐˜€ Lakehouse and Warehouse design on OneLake, Fabric Data Factory pipelines, semantic models with ๐—ฅ๐—ผ๐˜„-๐—Ÿ๐—ฒ๐˜ƒ๐—ฒ๐—น ๐—ฆ๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† (๐—ฅ๐—Ÿ๐—ฆ), and report publishing as Fabric Apps for internal teams and external stakeholders. โœฆ ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ & ๐——๐—ฎ๐˜๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ๐—ธ๐˜€ ๐—ฃ๐—ถ๐—ฝ๐—ฒ๐—น๐—ถ๐—ป๐—ฒ๐˜€ ADF orchestrated cloud pipelines and PySpark based distributed data processing on Databricks for large-scale, partitioned datasets. โœฆ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ & ๐—ฆ๐—ฆ๐—ฅ๐—ฆ ๐—ฅ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜๐—ถ๐—ป๐—ด Semantic model design, DAX measures, drill-through dashboards, RLS enforcement, SSRS and Report Builder reports, and Fabric App deployment for enterprise stakeholders. โœฆ ๐—Ÿ๐—ฒ๐—ด๐—ฎ๐—ฐ๐˜† ๐— ๐—œ๐—ฆ ๐— ๐—ถ๐—ด๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป Migrated 20+ reports from legacy systems into a centralized, modern BI architecture without disrupting ongoing operations. ๐—ฅ๐—˜๐—–๐—˜๐—ก๐—ง ๐—ฅ๐—˜๐—ฆ๐—จ๐—Ÿ๐—ง๐—ฆ โ€ข Reduced ETL runtime by ๐Ÿฏ๐Ÿด% (4 hrs โ†’ 2.5 hrs) by optimizing metadata-driven SSIS pipelines โ€ข Built automated SFTP ingestion pipelines with archive logic to ensure ๐—ถ๐—ป๐—ฐ๐—ฟ๐—ฒ๐—บ๐—ฒ๐—ป๐˜๐—ฎ๐—น, ๐—ฑ๐˜‚๐—ฝ๐—น๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ-๐—ณ๐—ฟ๐—ฒ๐—ฒ data loading โ€ข Delivered ๐—บ๐˜‚๐—น๐˜๐—ถ๐—ฝ๐—น๐—ฒ ๐—˜๐—ป๐˜๐—ฒ๐—ฟ๐—ฝ๐—ฟ๐—ถ๐˜€๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—ช๐—ฎ๐—ฟ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ฒ๐˜€ supporting different business products across fintech, billing, and payments โ€ข Published ๐Ÿญ๐Ÿฑ+ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ ๐—ฟ๐—ฒ๐—ฝ๐—ผ๐—ฟ๐˜๐˜€ as Fabric Apps with Row Level Security for external stakeholders โ€ข Onboarded 10+ new source tables into a redesigned data warehouse while improving ETL performance and storage efficiency โ€ข Worked extensively with ๐—ง๐—•-๐˜€๐—ฐ๐—ฎ๐—น๐—ฒ ๐˜๐—ฟ๐—ฎ๐—ป๐˜€๐—ฎ๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐—ฎ๐—น ๐—ฑ๐—ฎ๐˜๐—ฎ in a high-volume payment processing environment. ๐—–๐—ข๐—ฅ๐—˜ ๐—ฆ๐—ง๐—”๐—–๐—ž ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐—™๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ | ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—™๐—ฎ๐—ฐ๐˜๐—ผ๐—ฟ๐˜† | ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ ๐——๐—ฎ๐˜๐—ฎ๐—ฏ๐—ฟ๐—ถ๐—ฐ๐—ธ๐˜€ | ๐—ฃ๐˜†๐—ฆ๐—ฝ๐—ฎ๐—ฟ๐—ธ | ๐—”๐—ฝ๐—ฎ๐—ฐ๐—ต๐—ฒ ๐—ฆ๐—ฝ๐—ฎ๐—ฟ๐—ธ | ๐—ฆ๐—ฆ๐—œ๐—ฆ | ๐—ฆ๐—ค๐—Ÿ ๐—ฆ๐—ฒ๐—ฟ๐˜ƒ๐—ฒ๐—ฟ | ๐—ข๐—ฟ๐—ฎ๐—ฐ๐—น๐—ฒ | ๐—ฃ๐—ผ๐˜€๐˜๐—ด๐—ฟ๐—ฒ๐—ฆ๐—ค๐—Ÿ | ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ | ๐—ฆ๐—ฆ๐—ฅ๐—ฆ | ๐—ง-๐—ฆ๐—ค๐—Ÿ | ๐—ฃ๐—Ÿ/๐—ฆ๐—ค๐—Ÿ | ๐——๐—ฎ๐˜๐—ฎ ๐—ช๐—ฎ๐—ฟ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ถ๐—ป๐—ด | ๐— ๐—ฒ๐—ฑ๐—ฎ๐—น๐—น๐—ถ๐—ผ๐—ป ๐—”๐—ฟ๐—ฐ๐—ต๐—ถ๐˜๐—ฒ๐—ฐ๐˜๐˜‚๐—ฟ๐—ฒ | ๐—ฆ๐˜๐—ฎ๐—ฟ ๐—ฆ๐—ฐ๐—ต๐—ฒ๐—บ๐—ฎ | ๐—ฆ๐—ป๐—ผ๐˜„๐—ณ๐—น๐—ฎ๐—ธ๐—ฒ ๐—ฆ๐—ฐ๐—ต๐—ฒ๐—บ๐—ฎ | ๐—˜๐—ง๐—Ÿ/๐—˜๐—Ÿ๐—ง | ๐—Ÿ๐—ฎ๐—ธ๐—ฒ๐—ต๐—ผ๐˜‚๐˜€๐—ฒ ๐—•๐—˜๐—ฆ๐—ง-๐—™๐—œ๐—ง ๐—ฃ๐—ฅ๐—ข๐—๐—˜๐—–๐—ง๐—ฆ โ€ข Data warehouse or lakehouse design from scratch โ€ข ETL/ELT pipeline build, optimization, or troubleshooting โ€ข Microsoft Fabric or Azure migration from legacy on-prem systems โ€ข Power BI, SSRS, or Fabric App reporting solutions โ€ข SQL performance tuning, stored procedures, and indexing โ€ข Production pipeline monitoring, job scheduling, and failure resolution ๐—›๐—ข๐—ช ๐—œ ๐—ช๐—ข๐—ฅ๐—ž I understand your business process, data sources, and reporting needs first. Then I design a practical architecture, build clean and observable pipelines, validate the data, and deliver reporting ready models your team can actually trust with ๐—น๐—ผ๐—ด๐—ด๐—ถ๐—ป๐—ด, ๐—ฒ๐—ฟ๐—ฟ๐—ผ๐—ฟ ๐—ต๐—ฎ๐—ป๐—ฑ๐—น๐—ถ๐—ป๐—ด, and ๐—ท๐—ผ๐—ฏ ๐˜€๐—ฐ๐—ต๐—ฒ๐—ฑ๐˜‚๐—น๐—ถ๐—ป๐—ด built in from day one, not added as an afterthought. ๐Ÿ“ฉ ๐—ฆ๐—ฒ๐—ป๐—ฑ ๐—บ๐—ฒ ๐—ฎ ๐—บ๐—ฒ๐˜€๐˜€๐—ฎ๐—ด๐—ฒ ๐˜„๐—ถ๐˜๐—ต ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜ ๐—ฑ๐—ฒ๐˜๐—ฎ๐—ถ๐—น๐˜€. ๐—œ ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ผ๐—ป๐—ฑ ๐—พ๐˜‚๐—ถ๐—ฐ๐—ธ๐—น๐˜† ๐—ฎ๐—ป๐—ฑ ๐˜„๐—ถ๐—น๐—น ๐—ผ๐˜‚๐˜๐—น๐—ถ๐—ป๐—ฒ ๐—ฎ ๐—ฐ๐—น๐—ฒ๐—ฎ๐—ฟ ๐—ฎ๐—ฝ๐—ฝ๐—ฟ๐—ผ๐—ฎ๐—ฐ๐—ต ๐—ณ๐—ผ๐—ฟ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฝ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜.

  • Data Engineering
  • ETL Pipeline
  • Microsoft Azure
  • Microsoft Power BI
  • Databricks Platform
  • Data Warehousing
  • Data Lake
  • SQL
  • Data Modeling
  • SQL Server Integration Services
  • SQL Server Reporting Services
  • Microsoft SQL Server
  • Oracle
  • Fabric
  • Database Development
  • PySpark
  • Business Intelligence
  • PostgreSQL
  • Microsoft Power BI Data Visualization
  • Big Data
ijlal A.

Gilgit, Pakistan

$11/hr
5.0
8 jobs

Your data is sitting in spreadsheets, scattered across systems, or stuck in reports nobody reads. I fix thatโ€”turning raw, messy data into clean pipelines, automated workflows, and dashboards your team actually uses. I'm a data analyst & engineer based in Pakistan with a BSc in software engineering and hands-on experience building end-to-end data solutionsโ€”from raw ingestion to production-ready insights. ๐Ÿ”ง What I deliver: โ€ข Python scripts that automate hours of manual data work โ€ข SQL pipelines that clean, transform, and model your data correctly โ€ข ETL workflows connecting your data sources into one reliable system โ€ข PostgreSQL/MySQL database setup, optimization, and querying โ€ข Power BI & Looker Studio dashboards with KPIs your team will actually use โ€ข Web scrapers that collect structured data from any source โ€ข CSV/Excel data cleaning, normalization, and transformation ๐Ÿ“Œ Flagship project โ€” AETHER (Ethical Data Platform): Built a full-stack data platform with FastAPI, PostgreSQL, and Next.js featuring a 16-stage automated pipeline, two-tier RBAC with a 24-permission matrix, AES-256 encryption, append-only audit logs, and bias detection using SHAP and Fairlearn. This is not a tutorial project โ€” it is production architecture. ๐Ÿ“Œ Research โ€” Duolingo Churn Prediction (IEEE-track): Built and compared 6 ML models (Logistic Regression, Random Forest, XGBoost, LightGBM, MLP, LSTM) on 25,000+ user records. Deep learning models achieved 94%+ ROC-AUC. Paper targeting IEEE publication. โœ… 100% Job Success Score | 5.0 Rating | 8 completed contracts ๐ŸŽฏ Hire me if: โ€ข Your team spends hours on manual reporting that should be automated โ€ข You need a reliable pipeline built once, that works every time โ€ข You want someone who understands both the data and the business behind it โ€ข You need clean, documented, maintainable codeโ€”not a quick hack I respond within hours and deliver on time. Let's talk about your data.

  • Python
  • SQL
  • PostgreSQL
  • ETL
  • ETL Pipeline
  • MySQL
  • Data Cleaning
  • Data Management
  • Data Engineering
  • Data Analytics & Visualization Software
  • Data Integration
  • Business Intelligence
  • Data Modeling
  • dbt
  • Microsoft Excel
  • Web Scraping
  • Dashboard
James E.

Alimosho, Nigeria

$25/hr
4.7
5 jobs

Data is rarely perfectly clean. Integrations break, dashboards report the wrong revenue, and business logic gets lost in translation between the engineering team and the commercial team. That is where I come in. Hi, I'm James. I am a Senior Data Analyst and Analytics Engineer with 4+ years of experience building reliable data infrastructure for the telecom and fintech sectors. Most founders and technical leads find me when they are searching for an Analytics Engineer to build dbt pipelines, a Data Analyst to map out revenue models, or an expert to optimize slow SQL Server databases and fix broken Power BI reporting. I do not just slap a patch on a symptom. I specialize in commercial diagnosticsโ€”tracing reporting anomalies back to the root database schema, fixing the underlying business logic, and architecting systems that scale. What I engineer for my clients: ยฐ Analytics Engineering & Pipelines: Designing automated ETL/ELT frameworks, managing version-controlled dbt models, and structuring high-velocity transaction data so it is ready for analysis. ยฐ Commercial Data Diagnostics: Investigating operational bottlenecks, cleaning messy datasets, and performing root-cause analysis on data discrepancies to prevent downstream reporting failures. ยฐ Business Intelligence & Data Warehousing: Developing automated, executive-ready Power BI and Metabase dashboards backed by clean dimensional modeling and optimized SQL queries (PostgreSQL, T-SQL, MySQL). The Communication Advantage: Alongside my technical builds, I have spent years as a Lead Technical Tutor. What this means for you is that I know how to translate heavy data engineering concepts into plain English for non-technical stakeholders. I document my architecture thoroughly, communicate clearly, and ensure your team actually understands the infrastructure we build together. If you need someone who can own the data layer from the raw database extraction all the way to the final commercial dashboard, let's talk. Send me a message, and we can discuss how to bring structure and visibility to your data operations.

  • Business Intelligence
  • Data Analysis
  • Data Engineering
  • Data Analytics
  • Microsoft Power BI
  • Data Visualization
  • SQL
  • Microsoft SQL Server
  • PostgreSQL
  • Python
  • dbt
  • Data Warehousing
  • ETL Pipeline
  • Data Modeling
  • Microsoft Azure
  • A/B Testing
  • Governance, Risk Management & Compliance
  • Microsoft Excel
  • Dashboard
  • Streamlit
Abdullah K.

Karachi, Pakistan

$5/hr
5.0
2 jobs

Hi, I'm Computer Science graduate and passed in 2019. I have 7+ years of working experience on Data Engineering and Software Development domain. Qualification: โ€ข BS-Computer Science โ€ข IBM Data Science Specialization โ€ข Google Data Engineering Learning Path โ€ข Data Engineering Essentials using SQL,Python, and PySpark โ€ข Informatica BDM (Self-paced training from Informatica University). โ€ข Tableau Desktop Training Experience: โ€ข I have worked on GCP suite Big Data Management for a USA based client and other different clients which involves all the domain related to data engineering and administration. โ€ข I have worked on Informatica BDM tool for the ETL purpose for multiple projects โ€ข I have worked on different DBs like Teradata, Microsoft SQL Server Management Studio, NEO4J, Oracle Etc. โ€ข I have professional grip in programming languages like Python, C, C# and SQL. โ€ข Expert in scalable ETL Development, optimized workflow ETL cycle, data crunching using python. โ€ข Highly effective in Database Modeling, Data Architecture, Solution Design, Data Warehousing, Data Lake & Big Data, Data Analytics, Web Development and ERP systems. โ€ข Progressive leader to lead the team or project If you're still here. Hope you're interested. Just leave a message. I'll get back to you ASAP. Thank You! Regards, Abdullah Shaheen Khan

  • Informatica
  • Big Data
  • SQL
  • Python
  • Data Analysis
  • Snowflake
  • ETL Pipeline
  • Data Warehousing
  • Data Lake
  • Google Cloud Platform
  • Teradata
  • Microsoft SQL Server
  • Data Engineering
  • Data Extraction
  • Data Cleaning
Vivekk J.

Chennai, India

$40/hr
5.0
22 jobs

โ€ข Having more than decade of work experience with data. โ€ข 11 plus years of strong work experience in Informatica tools. โ€ข Good experience in Powercenter, IICS, DEI/BDM, IDQ, EDC, Axon and CDGC. โ€ข Extensively worked with different data sources: non-relational sources such as delimited and fixed width flat files and relational sources like Oracle, Snowflake and Sales Force โ€ข Had Experience in Data Modeling using Star/Snowflake modeling, Fact and Dimension tables. โ€ข Good exposure in Data Warehousing concepts and creation of logical and physical models โ€ข Had knowledge on Kimball/Inmon methodologies. โ€ข Familiar with UNIX command and shell scripting.

  • Informatica Data Quality
  • Informatica
  • SQL
  • Snowflake
  • Hive
  • Axon
  • Informatica Cloud
  • Unix
  • Data Warehousing & ETL Software
  • Data Warehousing
Thon M.

Phnom Penh, Cambodia

$30/hr
4.0
1 jobs

Cut a US retail client's cloud data costs by 40% by migrating their Informatica + Snowflake pipelines to Databricks. I'm a SnowPro Core Certified data engineer specializing in: โ€ข Legacy migrations โ€ข Pipeline re-engineering for cost & performance โ€ข Production-grade architectures for retail, FMCG, and agri-food Tech stack: Snowflake, Databricks, Informatica (on-prem & cloud), Python, SQL, Apache Spark Available 20-30 hrs/week. Let's talk about your data problem.

  • Python
  • Apache Spark
  • Informatica Cloud
  • SQL Programming
  • ETL Pipeline
  • Snowflake
  • Databricks Platform
  • PySpark
  • dbt
  • Apache Airflow
  • CI/CD

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What does an Informatica data quality freelancer do?

An Informatica data quality freelancer builds and manages data cleansing pipelines using the Informatica Data Quality platform to turn raw, inconsistent datasets into trusted business assets. This specialist profiles source systems to uncover structural errors, defines precise validation logic, and executes automated routines that standardize formats and remove duplicate records. The work centers on configuring reusable transformations within Informatica Developer and monitoring outcomes through detailed scorecards stored in the profiling warehouse. Clients rely on these experts to establish measurable quality benchmarks and route exception tasks for manual review when automated rules cannot resolve ambiguous matches.

  • Profile large datasets to discover content structures and compute baseline quality metrics that reveal missing values or format inconsistencies. The freelancer uses Informatica Analyst tools to generate these profiles and identifies specific areas where data fails to meet business standards before any cleansing occurs.
  • Define and implement complex data quality rules for validation, parsing, standardization, and enrichment within Informatica Developer mappings. These rules transform messy inputs into consistent outputs by applying reference data and custom logic, ensuring that addresses, names, and codes follow a unified format across all enterprise systems.
  • Build duplicate detection and identity matching logic to merge fragmented customer or product records into single golden views. The specialist configures field matching algorithms, reviews match scores to minimize false positives, and exports clean results while routing uncertain cases to exception queues for further human remediation.
  • Create and maintain scorecards that track data quality progress over time and store configuration statistics in the profiling warehouse. These visual reports allow stakeholders to monitor improvement trends, verify that cleansing rules perform as expected, and identify new data degradation issues as soon as they emerge in source systems.

How to hire an Informatica data quality freelancer on Upwork

Step 1: Post a job

Define your data profiling and cleansing needs clearly to attract qualified specialists. Use the Job Post Generator powered by Umaโ„ข, Upwork's Mindful AI to draft a precise description in seconds. Describe your requirements in a few sentences, and Uma creates a tailored post for this role. You can write a new post, update a saved draft, or reuse an existing one.

  • Specify whether you need rule development in Informatica Developer or scorecard monitoring in Analyst tools.
  • List specific datasets requiring validation, parsing, standardization, or enrichment logic.
  • Clarify if the work involves building duplicate detection models or configuring exception handling workflows.

Step 2: Evaluate candidates

Look for proven experience with Informatica Data Quality transformations and matching logic. Uma runs instant video interviews and builds shortlists with side-by-side comparisons to speed up your review. Focus on candidates who demonstrate clear methods for measuring data health.

  • Review portfolios for examples of configured validation rules and reusable transformation mappings.
  • Check for delivered scorecards that track data quality progress over time in monitored datasets.
  • Verify experience with identity matching outcomes and field matching performance evaluations.

Step 3: Interview your top choices

Discuss technical approaches to data profiling and rule implementation during live conversations. Schedule and conduct interviews within Upwork Messages, which generates an immediate transcript and summary after each session. Ask about their process for handling complex cleansing tasks.

  • Ask how they configure reference data to support accurate parsing and standardization tasks.
  • Discuss their method for reviewing match scores and resolving duplicate analysis exceptions.
  • Request examples of how they store statistics and configuration in the profiling warehouse.

Step 4: Agree on scope and begin work

Set clear milestones for profile generation, rule deployment, and scorecard delivery. Use Upwork Messages and the contract workroom for all communication and project management tasks. Identity verification, payment protection, hourly tracking, and project funds add security to every engagement.

  • Define deliverables such as executed data quality processes and exception task outputs for remediation.
  • Agree on metrics for success, including specific data quality measurements and rule evaluation results.
  • Establish a schedule for generating scorecards to monitor ongoing data health improvements.

Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.

The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.

How much does hiring an Informatica data quality freelancer cost?

$500-$1,500 per project is a typical range for focused Informatica data quality freelancer work. Final pricing depends on scope, technical complexity, required integrations, source-material quality, revision needs, and the freelancer's experience level.

Data profiling and assessment

$500-$1,200/project

Entry-level to mid-level
  • Generated dataset content and structure analysis
  • Computed baseline data quality measurements
  • Documented rule evaluation outputs and gaps

Rule configuration and cleansing

$1,200-$2,500/project

Mid-level
  • Defined validation, parsing, and standardization logic
  • Built reusable Informatica Developer transformations
  • Configured execution process for data enrichment

Duplicate detection and matching

$2,500-$4,500/project

Mid-level to senior-level
  • Built field matching and identity detection rules
  • Generated match performance and scorecard results
  • Exported exception lists for duplicate review

Scorecard implementation and monitoring

$4,500-$7,000/project

Senior-level
  • Configured tracking for data quality progress
  • Stored statistics and configuration in profiling warehouse
  • Published views for ongoing quality measurement

End-to-end DQ architecture

$7,000-$12,000/project

Expert-level
  • Mapped full lifecycle from profiling to governance
  • Connected DQ processes with Data Governance and Catalog
  • Automated routing for remediation and review tasks

Frequently asked questions

Is hiring an Informatica data quality freelancer worth it?

For most businesses, yes: hiring an Informatica data quality freelancer is worthwhile. These specialists configure profiling rules and build cleansing logic that automated tools cannot define on their own. You gain immediate access to expertise in identity matching and scorecard configuration without training internal staff on Informatica Developer.

How do I evaluate Informatica data quality freelancer candidates?

Review their experience with specific Informatica Data Quality workflows such as building reusable transformations for standardization. Ask candidates to describe how they configured match rules for duplicate detection and how they used scorecards to track data quality progress over time.

What deliverables should I expect from an Informatica data quality freelancer?

You should receive configured data quality rules for validation and parsing along with data profiles that measure dataset structure. The freelancer also submits duplicate analysis results and generates scorecards to monitor quality metrics in the profiling warehouse.

Can an Informatica data quality freelancer handle exception management?

Yes, these freelancers configure workflows to route exception tasks for manual review and remediation. They set up the system to flag records that fail validation rules so your team can correct specific data errors.