Data Integration
Connecting data from different systems, platforms and sources into reliable downstream pipelines.
Making Data Trustworthy.
Chandraprakash Jha
Data Quality & Data Engineering Expert
I help organizations turn unreliable, fragmented data into accurate, trusted and usable data.
Chandraprakash JhaData Engineering & Data Quality
At a Glance
The Short Version
0+
Years of Experience
0
Industry Domains
Data Engineering
Data Quality
Data Governance Core Specialization
Data Quality
Data Governance Core Specialization
What I Do
Turning Data Problems Into Trusted Data
Data Ingestion
Designing reliable mechanisms to bring data from multiple sources into data platforms.
Data Cleaning
Identifying inconsistencies, duplicates, missing values and structural problems in datasets.
Data Quality
Building processes and checks that ensure data is accurate, complete, consistent, timely and usable.
Data Governance
Helping organizations establish standards, ownership, controls and processes around their data.
Data Reliability
Finding and preventing data failures before they impact business users and downstream systems.
The Building Blocks
Six Dimensions of Data Quality
Every quality problem I've ever debugged traces back to one or more of these. None of them matter in isolation, they matter together.
Trusted
Data
Data
Accuracy
Completeness
Consistency
Validity
Uniqueness
Timeliness
- Accuracy
- Does the data reflect reality?
- Completeness
- Are the required fields actually populated?
- Consistency
- Does the same fact agree with itself across systems?
- Validity
- Does the data conform to the expected format and rules?
- Uniqueness
- Is each real-world entity represented once?
- Timeliness
- Is the data current enough to act on?
“Bad data creates bad decisions.”
Messy / Fragmented Data
Engineering
Quality Controls
Governance
Trusted Data
Better Decisions
Experience
8+ Years Across Data-Intensive Industries
I've worked where data is complex, high-volume and consequential when it goes wrong.
EdTech
Student, learning and platform data that must stay accurate as it scales.
Cybersecurity
High-volume security and event data where timeliness and accuracy matter.
Healthcare
Sensitive, accuracy-critical data with little tolerance for error.
Utilities
Operational and infrastructure data feeding real-world decisions.
Telecom
Large-scale transactional and network data across many systems.
The Problems I Solve
When Data Gets Complicated, I Help Make It Reliable.
- “The same customer exists in multiple systems.”
- “Different systems report different numbers.”
- “Data arrives incomplete or late.”
- “Nobody knows which dataset is trustworthy.”
- “Data pipelines fail silently.”
- “Business teams don't trust the reports.”
- “Data standards are inconsistent.”
- “Quality issues are discovered too late.”
My role: Find the problem→ Fix the data flow→ Improve quality→ Establish controls→ Make the data trustworthy.
Approach
How I Think About Data
Source
Ingest
Integrate
Transform
Validate
Govern
Trust
Good data engineering isn't just about moving data from A to B. It's about making sure what arrives at the destination is actually usable.
Good data isn't just data that exists.
Good data is data that people can trust.
- Available data≠usable data
- Integrated data≠trusted data
- A pipeline that runs≠a reliable data system
Quick Assessment
What's Your Data Trust Score?
Eight quick questions about how your data actually behaves day to day. An illustrative self-check, not a formal audit, just a fast way to see where trust is leaking.
Technical Expertise
Where I Work in the Stack
Data Engineering
Data pipelines, ETL/ELT, data ingestion, integration, transformation.
SQL, Python, Databricks, Snowflake, Azure Data Factory, AWS Glue, Airflow, Talend
Data Quality
Data profiling, validation, anomaly detection, quality rules, reconciliation.
SQL, Python, Great Expectations
Data Governance
Data standards, metadata, lineage, ownership, controls.
Collibra
Data Operations
Monitoring, troubleshooting, reliability, root-cause analysis.
Databricks, Azure Data Factory, AWS Glue, Airflow, SQL/Python-based checks
Data Architecture
Data flows, source-to-target mapping, system integration, scalable data processing.
Databricks, Snowflake, Azure Data Factory, AWS Glue, Talend
For Technical Reviewers
Technical Depth
The reasoning behind the summary above. Expand any section.
Data Architecture & Integration Patterns
Most integration problems come from systems that were never designed to talk to each other. I map source-to-target relationships explicitly before writing a pipeline: which system owns which field, what the canonical identifier is, and where divergence between systems is expected versus where it's a bug. Getting this mapping right up front avoids most of the rework that shows up later.
Validation Approaches
Validation belongs at two points: as data enters a system, and again before it reaches the people relying on it. Rules should be specific to what the data is actually used for, not a generic checklist applied everywhere. A field that's optional for one report can be mission-critical for another, and the validation logic needs to reflect that difference.
Governance & Ownership
Standards only hold up if someone is accountable for them. I focus on making ownership explicit for every dataset that matters: who defines what "correct" means, who gets alerted when it drifts, and who has the authority to fix it. Without that, even well-designed rules decay within a few months.
Monitoring & Automation
A pipeline that runs without errors isn't the same as a pipeline producing correct output. I build monitoring around the data itself, not just job status: freshness, volume and distribution checks that catch drift before a business user does, with alerts routed to whoever actually owns the fix.
Making Data Trustworthy.
I don't just work with data.
I help organizations trust it.
Contact
Let's Talk About Data
Working on a data quality, data engineering, integration or governance challenge? Let's connect.