Data Platform EngineerDepartment: Data & Analytics
Location: Hybrid
Employment Type: Full-Time
Position OverviewWe are seeking an experienced Data Engineer to join our growing team and play a central role in shaping our data strategy. This individual will be responsible for building, optimizing, and governing our Databricks-based data platform hosted on AWS connecting disparate data sources, enabling reliable data pipelines, and ensuring that high-quality data is available to support business intelligence, analytics, and machine learning initiatives.
Key ResponsibilitiesData Strategy & Platform Governance - Partner with leadership to define and execute a comprehensive data strategy aligned with business goals.
- Establish standards and best practices for data ingestion, transformation, storage, and access across the organization.
- Drive adoption of data governance frameworks including data cataloging, lineage tracking, and quality monitoring.
- Champion a culture of data-driven decision-making by enabling self-service analytics capabilities.
Databricks Platform Administration - Architect, configure, and maintain the Databricks environment hosted on AWS (EMR, S3, IAM, VPC, etc.).
- Manage Databricks workspaces, clusters, Unity Catalog, and access controls.
- Optimize compute and storage costs through cluster sizing, auto-scaling, and lifecycle management.
- Implement and maintain CI/CD pipelines for data workflows using tools
Data Integration & Pipeline Development - Design and build scalable ETL/ELT pipelines to ingest data from a variety of sources including SaaS applications, databases, APIs, and streaming platforms.
- Leverage Delta Lake, Delta Live Tables, and Databricks Workflows to build reliable, incremental data processing solutions.
- Integrate cloud-native AWS services (Glue, Kinesis, EventBridge, Lambda) with the Databricks ecosystem.
- Ensure data pipeline reliability through monitoring, alerting, and automated recovery mechanisms.
Collaboration & Data Enablement - Partner with data analysts, data scientists, and business stakeholders to understand data requirements and deliver fit-for-purpose datasets.
- Develop and maintain data documentation, including data dictionaries, runbooks, and architecture diagrams.
- Mentor junior team members on best practices in data engineering and cloud-native development.
Required QualificationsTechnical Skills - 5+ years of hands-on data engineering experience in a cloud environment.
- Strong proficiency with Databricks (Notebooks, Workflows, Delta Lake, Unity Catalog, SQL Warehouses).
- Deep expertise with AWS services: S3, IAM, VPC, Glue, Redshift, RDS, Lambda, Kinesis, or EventBridge.
- Proficiency in Python for data engineering workloads; SQL expertise required.
- Experience designing and building data lake / lakehouse architectures.
Familiarity with data transformation tooling such as dbt, Apache Spark, or similar frameworks.
- Knowledge of data modeling concepts (dimensional modeling, data vault, or medallion architecture).
Experience & Education - Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience.
- Proven track record of delivering end-to-end data platform solutions at scale.
- Experience with infrastructure-as-code tools such as Terraform is a strong plus.
- Databricks Certified Associate or Professional certification preferred.
- AWS Certified Data Analytics or Solutions Architect certification preferred
We are not able to consider candidates who will require sponsorship at any point during employment.EEO/Vets/Women/Minorities/Disabled
www.bennettig.com