Smartbridge

Data Engineer

• Define the target-state Azure data architecture (ingestion, orchestration, storage zones, serving patterns), security/networking boundaries, cost/perf tradeoffs, and promotion strategy (Dev→Test→Prod).
• Implement robust ELT/ETL with ADF/Synapse Pipelines (parameters, reusable templates, CI/CD).
• Hands-on in T-SQL and Python/PySpark for transformations, utilities, and tests.
• Physical/semantic modeling, partitioning, columnstore strategies, statistics management, query plan analysis, index design, concurrency & transaction isolation, workload management.
• SLA/SLO definitions, Azure Monitor / Log Analytics / App Insights dashboards and alerts; error handling, retries/backoff, idempotency, CDC and schema drift strategies.
• RBAC, Key Vault, managed identities, private endpoints/VNet, data masking patterns; document data contracts and access patterns.
• Code reviews, PR discipline, mentoring, and crisp documentation/runbooks for client handoff.

Senior Azure Data Engineer

QA Engineer

• Participate in business requirements sessions, assist with discovery, capture requirements, user stories, and acceptance criteria.
• Define risk-based test plans (functional, integration, regression, UAT) focused on high-value paths.
• Validate data ingestion, transformation, and aggregation logic across Azure data pipelines
• Perform source-to-target (S2T) reconciliation and data completeness checks
• Ensure data accuracy, consistency, timeliness, and integrity across layers (raw, curated, semantic)
• Perform regression testing for data model and pipeline changes
• Maintain traceability (requirements → test cases → defects → release notes).
• Plan and facilitate UAT (scripts, execution, acceptance sign-off); manage change requests.
• Communicate status and risks clearly to client stakeholders and internal leadership.

Senior Azure Data Engineer

Quality Analyst, Client-Facing, BA, QA