Is this actually your fit?
Three short trait quizzes scored against this exact role. No card. ~10 minutes — less if you've already done some.
Every career on ClarUP carries a 6-trait blueprint scored from real practitioners. Take the trait quizzes to see your fit.
High Conscientiousness92/100
The strongest signal for this role. People who score 70+ on this dimension report higher day-to-day satisfaction.
Three short trait quizzes scored against this exact role — your fit %, no card. ~10 minutes, less if you've already done some.
India-first salary signal — fresh-grad to leadership, the cities where it pays best, and what each level is worth on the open market.
IT services (TCS, Infosys, Wipro DQ practice) freshers ₹3.5-5.5L. Product unicorns (Razorpay, Meesho, PhonePe) entry DQA ₹5-8L. Mid-level DQ analyst at product companies ₹8-16L; at BFSI (HDFC, ICICI, Bajaj Finserv DQ teams) ₹7-14L. Senior/Lead at mature data orgs or FAANG-India ₹25-45L. Head of Data Quality at listed companies or large fintechs ₹40-60L.
Not the brochure version. The actual block-by-block reality of the role on a typical Tuesday.
Log into dbt Cloud and review the overnight test run — 3 failures across the payments domain flagged in Slack. Classify each by severity: one referential integrity break (P1), one null spike in payment_method (P2), one row-count drop in settlement summary (P2). Open Jira tickets and DM the responsible data engineers with context before standup.
Data engineering standup — present the 3 overnight failures with preliminary root-cause hypotheses, agree ownership, and flag that the settlement anomaly investigation may need Kafka consumer metrics from the platform team. Standup closes in 15 minutes.
Profile a new partner bank SFTP feed onboarded this sprint. Write SQL in DataGrip against the Snowflake staging layer: assess null rates per column, cardinality on account_id and ifsc_code, date format consistency on value_date, and check for duplicate transaction references. Document findings in Confluence.
Root-cause investigation on the settlement row-count anomaly. Pull Airflow DAG logs, query the Kafka consumer lag metrics dashboard, and compare yesterday's pipeline run against the previous 14-day baseline. Isolate a 2-hour gap caused by a Kafka partition rebalance overnight — document in Jira and tag the platform SRE team.
Lunch and a quick read of the DPDP Act compliance checklist update circulated by the Legal team — new guidance on personal data quality attestation affects the customer_master domain tests. Note two dbt test additions needed for the KYC consent flow.
Write and merge a new dbt test suite for the customer_master domain: not_null and unique tests on mobile_number and pan_hash, accepted_values on customer_tier, and a singular test verifying KYC-verified records always have a non-null consent_timestamp. Push PR, get data engineering review, merge by 4 PM.
Prepare and send the weekly data quality scorecard to the Finance VP and product data owners — domain-level DQ scores for completeness, validity, uniqueness, and timeliness; two SLA breaches called out with root causes; remediation owners and deadlines confirmed. Send on Slack and email.
Update the DQ metrics dashboard in Looker with the week's scorecard data. Reply to a question from the analytics team about why the daily_active_users metric showed a 5% drop last Thursday — pull the dbt model logs, confirm the upstream mobile event stream had a 45-minute gap due to an iOS SDK version rollout, and send a clear explanation to close the incident.
The real entry pathway for this role — eligibility, the qualifying exam, training, and licensing — in the order most people follow it.
Bachelor's degree in Computer Science, Information Technology, Statistics, Mathematics, or a related quantitative discipline. B.Tech / B.E. (CSE, IT), B.Sc Statistics / Mathematics, BCA, or B.Com Analytics concentration are all common entry routes in India.
M.Tech (Data Science / CSE), M.Sc Statistics, or PG Diploma in Data Science from institutes like IIT Madras Online, BITS Wilp, or ISB certificate programs. Master's is not a gate — a strong portfolio of dbt/GE test implementations weighs equally.
dbt Certified Developer (dbt Labs) directly validates the most-used QA testing framework. DAMA CDMP Associate signals data-quality methodology depth. Great Expectations Community contributor status is recognized at product-led data teams. Microsoft DP-900 (Azure Data Fundamentals) or Google Cloud Professional Data Engineer add warehouse-specific credibility.
Informatica Data Quality (IDQ) certification for enterprise MDM contexts (used at HDFC, Infosys, Wipro). Talend Data Quality module certification for IT-services-heavy roles. IBM InfoSphere DataStage QA module for legacy bank environments.
Junior QA engineer or manual tester transitioning into data QA via SQL upskilling and a dbt course is a recognized path at Indian IT services companies. Data Analyst with 1-2 years building dashboards who moves into pipeline monitoring is the most common product-side entry.
Core skills you must own, the support skills you'll grow into, and the tools you'll have open all day.
People already doing this work — and the rooms (subreddits, Discords, Slacks) where they hang out.
Anjul Bhambhri
SVP, Platform Engineering · Adobe (formerly VP, Big Data & Analytics Platform, IBM)
Sunil Soares
Founder & Managing Partner · Information Asset LLC (formerly Director of Information Governance, IBM)
Dr. Anil Kaul
Co-founder & CEO · Absolutdata
Ramkumar Ravichandran
Director, Data Science & AI · Convera (formerly Director, Data Science, Visa)
Anup Purohit
Global Chief Information Officer · Wipro (formerly CIO, Yes Bank)
dbt Community Slack
SlackThe primary global community for dbt practitioners with 50,000+ members. The #dbt-core, #testing, and #india channels are active for data quality discussions, dbt test patterns, and community support. Most DQAs using dbt for pipeline testing use this as their primary knowledge-sharing resource.
Great Expectations Community Discourse
Discourse forumThe official community forum for Great Expectations users. Covers custom expectation authoring, integration with dbt and Airflow, and data pipeline testing patterns. Active contributors include practitioners from Indian data teams at fintechs and e-commerce companies.
r/dataengineering
RedditThe largest data engineering community on Reddit with 250,000+ members. Covers data quality, pipeline testing, tooling comparisons (dbt vs GE vs Soda vs Monte Carlo), and career advice. Highly active for tooling discussions and real-world incident post-mortems relevant to DQA practitioners.
DAMA India
LinkedIn / in-personThe India chapter of DAMA International, the global professional body for data management. Organizes meetups in Bengaluru, Mumbai, and Hyderabad for data quality, governance, and CDMP certification study groups. Relevant for practitioners working on enterprise DQ frameworks at BFSI and IT-services companies.
The traps real practitioners wish someone had named for them in year one. Read these before you commit, not after.
Treating 'GE expectations passing' as equivalent to 'data quality assured'
Setting alert thresholds too tightly on noisy metrics, causing alert fatigue
Doing root-cause investigation before confirming the pipeline load completed
Defining data quality SLAs without a documented measurement methodology
The upside that makes this work worth it, set honestly against the parts people quietly resent. Both sides, before you commit.
Straight answers to what people genuinely wonder before stepping into this work — no brochure spin.
Books, longreads, and references practitioners come back to.
The Practitioner's Guide to Graph Data
by Denise Gosnell and Matthias Broecheler
dbt Documentation: Testing
by dbt Labs
Data Management Body of Knowledge (DMBOK), 2nd Edition
by DAMA International
Fundamentals of Data Engineering
by Joe Reis and Matt Housley
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