25+
Years DB Experience
30%
Operational Visibility ↑
1M+
Files Archived / Day
25%
Query Latency Reduction
01
Core Competencies
Relational Databases
- Oracle Database (9i–19c)
- Microsoft SQL Server
- Schema design & normalization
- Partitioning strategies
- Index optimization
PL/SQL & T-SQL
- Stored procedures & packages
- Triggers & cursors
- Bulk collect / FORALL
- Dynamic SQL
- Exception handling
ETL & Data Pipelines
- Automated ETL scripting
- 3GPP data processing (HSS/HLR)
- Metadata archiving (1M+ files/day)
- Shell + SQL integration
- Incremental load patterns
Performance & Tuning
- EXPLAIN PLAN / AWR reports
- Query rewrite & hints
- Table stats & histograms
- Parallel query execution
- Connection pool management
Security & Compliance
- SOX compliance enforcement
- Role-based access control
- Audit trail management
- Data masking & encryption
- GRC activities
Tooling & Integration
- SQL*Plus / SQL Developer
- SSMS / Azure Data Studio
- PowerShell + SQL Server
- HDFS / Hadoop integration
- Ansible DB automation
02
Proficiency Levels
Oracle DB / SQL*Plus95%
PL/SQL — Stored Procedures & Packages92%
Microsoft SQL Server / T-SQL88%
ETL Design & Automation90%
Performance Tuning & Query Optimization87%
PowerShell / Shell DB Scripting85%
03
Code Samples
-- ============================================================ -- Automated ETL Procedure: 3GPP CDR Staging to Core -- Platform: Oracle 19c | Context: AT&T / Verizon BSS -- ============================================================ CREATE OR REPLACE PROCEDURE etl_load_cdr_records ( p_batch_date IN DATE DEFAULT SYSDATE, p_rows_loaded OUT NUMBER ) AS v_batch_id NUMBER; v_count PLS_INTEGER := 0; ex_no_data EXCEPTION; BEGIN -- Generate batch identifier SELECT etl_batch_seq.NEXTVAL INTO v_batch_id FROM DUAL; -- Bulk insert from staging to core via MERGE MERGE INTO cdr_core tgt USING ( SELECT record_id, msisdn, call_type, duration_sec, TRUNC(call_ts) call_date, node_id FROM cdr_staging WHERE load_date = TRUNC(p_batch_date) AND status = 'PENDING' ) src ON (tgt.record_id = src.record_id) WHEN MATCHED THEN UPDATE SET tgt.status = 'RELOADED', tgt.batch_id = v_batch_id WHEN NOT MATCHED THEN INSERT (record_id, msisdn, call_type, duration_sec, call_date, node_id, batch_id, status) VALUES (src.record_id, src.msisdn, src.call_type, src.duration_sec, src.call_date, src.node_id, v_batch_id, 'LOADED'); v_count := SQL%ROWCOUNT; IF v_count = 0 THEN RAISE ex_no_data; END IF; UPDATE cdr_staging SET status = 'PROCESSED', batch_id = v_batch_id WHERE load_date = TRUNC(p_batch_date) AND status = 'PENDING'; p_rows_loaded := v_count; COMMIT; EXCEPTION WHEN ex_no_data THEN DBMS_OUTPUT.PUT_LINE('No records for batch date: ' || p_batch_date); WHEN OTHERS THEN ROLLBACK; RAISE; END etl_load_cdr_records; /
-- ============================================================ -- Billing Audit Query: SOX-Compliant Revenue Reconciliation -- Platform: Oracle | Context: AT&T Cingular Billing Cycles -- ============================================================ WITH daily_revenue AS ( SELECT TRUNC(txn_timestamp, 'DD') AS txn_date, account_type, SUM(charge_amount) AS gross_revenue, SUM(discount_amount) AS total_discounts, COUNT(DISTINCT subscriber_id) AS active_subs, SUM(CASE WHEN status = 'FAILED' THEN charge_amount ELSE 0 END) AS failed_revenue FROM billing_transactions WHERE txn_timestamp >= ADD_MONTHS(TRUNC(SYSDATE, 'MM'), -1) GROUP BY TRUNC(txn_timestamp, 'DD'), account_type ), variance_check AS ( SELECT d.*, LAG(gross_revenue, 1) OVER (PARTITION BY account_type ORDER BY txn_date) AS prev_day_rev, ROUND( (gross_revenue - LAG(gross_revenue,1) OVER(PARTITION BY account_type ORDER BY txn_date)) / NULLIF(LAG(gross_revenue,1) OVER(PARTITION BY account_type ORDER BY txn_date), 0) * 100, 2 ) AS day_over_day_pct FROM daily_revenue d ) SELECT txn_date, account_type, TO_CHAR(gross_revenue, '$999,999,990.00') AS gross_rev, TO_CHAR(total_discounts, '$999,999,990.00') AS discounts, active_subs, day_over_day_pct AS dod_variance_pct, CASE WHEN ABS(day_over_day_pct) > 15 THEN '⚠ FLAG FOR REVIEW' ELSE 'OK' END AS audit_status FROM variance_check WHERE failed_revenue / NULLIF(gross_revenue, 0) > 0.02 OR ABS(day_over_day_pct) > 15 ORDER BY txn_date DESC, account_type;
-- ============================================================ -- Performance Tuning: Index Strategy + Query Rewrite -- Platform: SQL Server | Context: Verizon App Support -- ============================================================ -- BEFORE: full table scan on 200M-row incident table (12.3s) -- SELECT * FROM incidents WHERE app_id = 'VZ-CORE' AND sev = 1 -- STEP 1: Composite covering index CREATE NONCLUSTERED INDEX IX_incidents_app_sev_ts ON dbo.incidents (app_id, severity_level, created_ts DESC) INCLUDE (incident_id, title, assigned_team, resolved_ts) WITH (ONLINE = ON, FILLFACTOR = 85); -- STEP 2: Rewritten query using CTE + filtered window WITH recent_sev1 AS ( SELECT incident_id, title, assigned_team, created_ts, resolved_ts, DATEDIFF(MINUTE, created_ts, ISNULL(resolved_ts, GETDATE())) AS mttr_min, ROW_NUMBER() OVER ( PARTITION BY assigned_team ORDER BY created_ts DESC ) AS rn FROM dbo.incidents WITH (NOLOCK) WHERE app_id = N'VZ-CORE' AND severity_level = 1 AND created_ts >= DATEADD(DAY, -30, GETDATE()) ) SELECT assigned_team, COUNT(*) AS incident_count, AVG(CAST(mttr_min AS DECIMAL(10,2))) AS avg_mttr_min, MAX(mttr_min) AS worst_mttr_min, MIN(CASE WHEN resolved_ts IS NULL THEN 1 ELSE 0 END) AS open_incidents FROM recent_sev1 GROUP BY assigned_team HAVING COUNT(*) > 2 ORDER BY avg_mttr_min ASC; -- AFTER: 0.4s execution — 30x improvement using covering index
04
Database Experience Timeline
Verizon
2020 – 2025
Tier 2 Production / App Support Engineer
- ◈Developed automated ETL scripts and PL/SQL queries that increased operational visibility by 30% across 72+ enterprise applications
- ◈Maintained SQL-backed runbook automation tied to Ansible/Jenkins, reducing Sev-1 MTTR by 85%
- ◈Optimized Oracle and SQL Server queries for BSS/CRM platforms running on Apache WebLogic
TEOCO Corp
2013 – 2019
Senior Systems Integration Engineer
- ◈Led database migration for AT&T Linux platform transitions, cutting resolution times by 80%
- ◈Maintained 98% SLA adherence managing BSS billing and CRM systems with complex PL/SQL packages
- ◈Built ETL automation pipelines integrating Oracle schemas with HDFS for telecom analytics
AIRCOM / TEOCO
2009 – 2013
Database Architect
- ◈Implemented metadata archiving system processing 1M+ files daily — enhanced retrieval efficiency dramatically
- ◈Developed HDFS-backed storage solutions reducing query latency by 25%
- ◈Designed Oracle schemas for GSM/UMTS/LTE OSS solutions across multi-vendor VMware deployments
AT&T
1999 – 2009
Implementation Solutions Engineer
- ◈Designed custom billing tools for AT&T and Cingular Wireless using Oracle and Shell scripting, supporting 24/7 billing cycles
- ◈Performed complex ETL processing for 3GPP data (HSS/HLR/MME) repositories for strategic planning
- ◈Enforced strict SOX compliance and enterprise security protocols across all Oracle provisioning activities
05
Tools & Platforms
Oracle DB 9i–19c
SQL*Plus
SQL Developer
SQL Server 2012–2019
SSMS
Azure Data Studio
PL/SQL
T-SQL
PowerShell + SQLCMD
HDFS Integration
Kafka + Oracle CDC
Toad for Oracle
Kibana (ELK)
New Relic DB Monitoring
Ansible DB Modules