Data Engineer (Santo Domingo)

We're hiring a Data Engineer to own the data layer across our product portfolio. This is a database-first role and a deliberately broad one: you'll write and optimize the T-SQL that our applications run on, design the schema new features are built against, and keep the pipelines that move data from ingestion through processing to reporting correct, fast, and auditable.

We're not hiring for a single project. You'll move between products and problem domains — real-time event ingestion, transactional processing, reporting and analytics, historical migrations — so we're looking for someone adaptable who gets productive in an unfamiliar schema quickly and is comfortable owning more than one system at a time.

You'll work across multiple database instances — development, staging, and production — and own the packaging and deployment path between them, so that what's checked into source control is provably what's running. Correctness is not negotiable: the queries you write decide what our applications return and what our reports say. You'll partner closely with the API and frontend engineers who consume your procedures, and you'll be the person who answers "why does this number look wrong?"

Tech Stack

• Database: Azure SQL, T-SQL, stored procedures, table-valued parameters (TVPs), user-defined functions, temp-table staging patterns, query plan analysis

• Packaging & Deployment: SQL Server Database Projects (.sqlproj / SSDT), SqlPackage and DACPAC publishing, schema compare and drift detection, versioned migration scripts, multi-instance (dev / staging / prod) deployment

• Data Movement & Analytics: Azure Storage Queues, Azure Table Storage, Azure Blob Storage, Azure Functions, queue drainer services, bulk upsert/apply patterns, Microsoft Fabric (Lakehouse / Warehouse / Data Factory pipelines / Power BI semantic models), CSV/Excel export generation

• Consuming Layers: ASP.NET Core (.NET 8) with Dapper, C# — enough to read and reason about how your procedures are called

• Infrastructure: Azure (SQL Database, Storage, Key Vault, Application Insights), Azure DevOps Pipelines, sqlcmd / Azure Data Studio / SSMS, Entra ID authentication

Responsibilities

• Design, write, and optimize T-SQL stored procedures for real-time ingestion, transactional processing, reporting, and application data access

• Own the schema for large relational databases (100+ tables) — tables, indexes, constraints, TVPs, and migration scripts

• Package and deploy database changes across multiple instances using SQL Database Projects and SqlPackage/DACPAC, and detect and resolve schema drift between environments

• Verify data integrity as a first-class deliverable — enforce and validate keys, constraints, and referential integrity; write the checks that catch orphaned, duplicated, and contradictory rows before they reach production consumers

• Build automated data-integrity and reconciliation checks that run continuously, not just when someone reports a problem

• Diagnose and fix query performance problems: execution plans, missing indexes, correlated subqueries, unbounded scans, and timeouts at production volume

• Build and maintain data pipelines that move high-volume event data from ingestion queues into processed, queryable, reportable records

• Write reconciliation queries that prove downstream output ties back to source data — and investigate when it doesn't

• Handle historical data migrations, backfills, and legacy-to-current data reconciliation

• Keep paged and non-paged (export) variants of reporting procedures in lockstep so the on-screen grid and the exported file never disagree

• Model and surface data in Microsoft Fabric for analytics and reporting workloads as we build that capability out

• Partner with API engineers on contract changes — parameter shape, result sets, and DTO mapping

• Contribute to CI/CD for database deployments and keep checked-in objects aligned with what's actually deployed

• Pick up unfamiliar schemas and legacy data models across different products and get to a working understanding fast

• Write reusable, well-documented SQL that the next engineer can safely change

Required Qualifications

• 3+ years professional experience in a database-focused engineering role (Data Engineer, Database Developer, or similar)

• Strong T-SQL — stored procedures, CTEs, window functions, temp tables, TVPs, dynamic SQL where it's warranted

• Demonstrable query optimization skills: reading execution plans, index design, and diagnosing slow queries at volume

• Relational schema design — normalization, keys, constraints, soft-delete and status-flag patterns, slowly-changing/point-in-time data

• Experience with SQL Server or Azure SQL in production

• Hands-on data integrity work — constraint and referential-integrity design, duplicate and orphan detection, and writing validation queries that prove a dataset is sound

• Experience working across multiple database environments and promoting schema changes between them

• Experience with database packaging/deployment tooling — SqlPackage, DACPAC, SSDT, Flyway, Liquibase, or equivalent

• Experience building or maintaining data pipelines / ETL processes

• Comfortable working with large datasets and reasoning about correctness, not just output

• Adaptable across products and domains — you've worked on more than one system and can context-switch without losing rigor

• Version control discipline for database objects (Git, migration scripts, or database projects)

• Able to read enough C# or another application language to understand how your data layer is consumed

Preferred Qualifications

• Microsoft Fabric experience — Lakehouse, Warehouse, Data Factory pipelines, Dataflows Gen2, OneLake, or Power BI semantic models

• SQL Server Database Projects (SSDT / .sqlproj) and SqlPackage/DACPAC publishing in a CI/CD pipeline

• Azure SQL specifics — DTU/vCore tuning, Query Store, elastic pools, managed identity authentication

• Azure Storage — Queues, Tables, and Blobs as part of a data pipeline

• Experience in domains where output must reconcile exactly and a wrong number has real consequences

• High-volume event or transactional data processing experience

• Point-in-time / temporal data modeling (values and configuration that must be resolved as of a historical timestamp)

• Azure Data Factory, SSIS, or equivalent orchestration tooling

• Dapper or micro-ORM data access patterns

• Azure DevOps (Pipelines, Boards, Repos)

• Python or PowerShell for data tooling and one-off analysis

What We Value

• Clear, direct communication — say what you mean, flag problems early, ask questions when something is unclear

• Verifying against the real object — read the deployed procedure, run the query, check the plan; don't trust documentation or assumptions about what the data does, and don't assume one instance matches another

• Fixing problems at the source instead of working around dirty data downstream

• Challenging risky or incorrect decisions instead of silently going along

• Treating data output as something that must be provably correct, with a query that demonstrates it

• Curiosity across the whole portfolio — willingness to learn a new domain rather than staying in one comfortable corner

• Pragmatism over over-engineering — solve the problem at hand without gold-plating

In order to apply, please submit your CV in English