Databricks Engineer Id86297

INGEPSY; Risaralda; Presencial; COP 280.260.000 - 435.961.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Professional growth

Competitive USD-based compensation

A selection of exciting projects

Flextime

Descripción de la vacante

INGEPSY seeks a Data Engineer to design, build, and operate batch and streaming data pipelines on Databricks using PySpark and Delta Lake. You will migrate legacy ETL workloads to a governed Lakehouse, modeling a medallion architecture for analytics and AI use cases.

The role requires strong SQL and Python, experience with Unity Catalog governance, and collaboration with DevOps and analytics engineers in an Agile environment. Col/work in Pereira, RIS.

Formación

• 4+ years of professional experience in data engineering with Spark and cloud data architectures.
• Hands-on experience with Databricks, PySpark, and Delta Lake for pipelines.
• Advanced SQL and Python with data modeling across Lakehouse patterns.
• Experience with Structured Streaming, Auto Loader, Kafka, or Event Hubs.

Responsabilidades

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion architecture serving analytics, reporting, and ML users.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with data parity.
• Write clean, well-tested Python and SQL; review code and document solutions.
• Optimize Spark jobs and Delta tables for performance and cost; tune partitions and caching.
• Implement data quality, lineage, and governance using Unity Catalog and tests.
• Collaborate with DevOps, platform, and analytics teams on security and observability.

Conocimientos

Apache Spark

Databricks

PySpark

Delta Lake

SQL

Python

Structured Streaming

Kafka

Event Hubs

Databricks Workflows

Airflow

Azure Data Factory

Unity Catalog

Data governance

PII handling

dbt

Security best practices

Terraform

Dynatrace

Herramientas

Terraform

Dynatrace

CloudWatch

PostgreSQL

Descripción del empleo

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Data Engineer to build batch and streaming pipelines on Databricks using PySpark and Delta Lake. This person migrates legacy data warehouse and ETL workloads onto a governed Lakehouse, modeling a medallion architecture that powers analytics and AI use cases. Strong SQL, Python, and experience with Unity Catalog governance round out the role.

WHAT YOU WILL DO

• Design, build, and operate batch and streaming data pipelines on Databricks using PySpark, Delta Lake, and Databricks Workflows.
• Model and maintain a medallion (bronze/silver/gold) architecture serving analytics, reporting, and machine learning consumers.
• Migrate legacy ETL and data warehouse workloads onto the Lakehouse with validated data parity and minimal business disruption.
• Use Claude or GitHub Copilot as a development accelerator, generating code scaffolding, writing and reviewing tests, creating documentation, and prototyping solutions.
• Write clean, well-tested Python and SQL; maintain high standards through code review and documentation.
• Optimize Spark jobs and Delta tables for performance and cost, including partitioning, clustering, caching, and cluster sizing.
• Implement data quality, lineage, and governance controls using Unity Catalog and automated validation checks.
• Debug, troubleshoot, and resolve pipeline failures, data defects, and production incidents.
• Participate in Agile or product-centric delivery practices, including sprint planning and retrospectives.
• Collaborate with DevOps, platform, and analytics engineers on observability, security, and compliance best practices.

MUST HAVES

• 4+ years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.
• Strong hands‑on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.
• Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.
• Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.
• Strong problem-solving, collaboration, and communication skills, including mentoring junior engineers and explaining data concepts to non-technical stakeholders.
• Familiarity with Unity Catalog, data governance, access control, and PII handling.
• Experience with dbt or an equivalent transformation framework.
• Familiarity with secure coding standards and industry security best practices.

NICE TO HAVES

• Experience with Infrastructure as Code (IaC) using Terraform and CI/CD using Azure DevOps.
• Experience working with relational databases (specifically PostgreSQL) and data persistence concepts.
• Familiarity with logging and monitoring tools (e.g., Dynatrace, CloudWatch, Databricks system tables).
• Experience working in Agile or team-based development environments preferred.

PERKS AND BENEFITS

• Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
• A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
• Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.

Requirements

• -+4 years of professional experience in data engineering, featuring direct expertise with Apache Spark and cloud-based data architectures.
• -Strong hands‑on experience building data pipelines with Databricks, Apache Spark (PySpark), and Delta Lake.
• -Advanced SQL and Python, with strong data modeling skills across dimensional and Lakehouse patterns.
• -Experience with streaming ingestion using Structured Streaming, Auto Loader, Kafka, or Event Hubs.
• -Experience with workflow orchestration (Databricks Workflows, Airflow, or Azure Data Factory).
• -Experience with legacy platform migrations, ETL modernization, or managing data hygiene when porting old systems.
• -Strong problem‑solving, collaboration, and communication skills, including mentoring junior engineers and explaining data concepts to non‑technical stakeholders.
• -Familiarity with Unity Catalog, data governance, access control, and PII handling.
• -Experience with dbt or an equivalent transformation framework.
• -Familiarity with secure coding standards and industry security best practices.
• -Experience delivering production data platforms at scale.

Databricks Engineer ID86297 • Pereira, RIS, co