Leads complex, enterprise-scale data engineering initiatives, architect cloud-native warehousing solutions, and mentor junior staff. You will design end-to-end ETL/ELT and analytics platforms using Databricks, Informatica, Python, SQL, and cloud services; set and enforce engineering standards; and engage stakeholders to translate business requirements into robust, secure, and performant data products that power analytics, reporting, and ML workflows.
This position is responsible for architecting and delivering cloud-native data platforms and pipelines that provide reliable, governed, and scalable data to the enterprise. Data Engineer III leads migrations to modern cloud warehouses, defines and enforces best practices (ETL design, coding standards, security), and builds modular, reusable components for analytics/reporting/ML. The role optimizes throughput, latency, and reliability, establishes SLAs and observability, and mentors engineers to uplift team capability.
Operates with broad autonomy, collaborating with executive and technical stakeholders to translate requirements into engineering specifications, cost-aware architectures, and operational runbooks. Ensures data governance (lineage, catalog, privacy), compliance, and resilience, while driving continuous improvement across performance tuning, incident response, and reliability engineering.
Essential Job Tasks:
- Architects and develops enterprise-scale cloud data pipelines and warehousing solutions.
- Leads migration initiatives from legacy platforms to cloud-native data warehouses.
- Defines and enforces ETL/ELT design standards, coding practices, and security controls.
- Builds modular, reusable data components for analytics, reporting, and ML feature pipelines.
- Optimizes pipeline performance (throughput, latency, reliability) and establish SLAs/monitoring.
- Provides technical leadership and mentor junior engineers; document frameworks and patterns.
- Engages stakeholders; translate business requirements into engineering specs and roadmaps.
- Governs data quality, lineage, and cataloging; ensure compliance with privacy/security policies.
Position Requirements:
Data Scientist II
- Bachelor’s Degree in Computer Science, Engineering, or related discipline + 2 years experience or Master’s Degree in Computer Science, Engineering, or related discipline + 1 year experience in data engineering (ETL/ELT, cloud warehousing, analytics) with advanced expertise in Databricks, Informatica, Python, SQL, and cloud data platforms.
- Must have strong background in cloud data architecture, governance, performance optimization, and technical leadership/communication.
- Must have experience in the following:
- Languages and platforms: Proficient in Python and SQL; Databricks (notebooks/jobs) and Informatica (ETL/ELT).
- Cloud platforms: Hands on with Azure/AWS/GCP; familiarity with storage, compute, IAM, networking basics.
- Data warehousing: Modeling (dimensional/star), partitioning, Delta/Parquet, medallion architecture; performance tuning.
- Orchestration and reliability: Job scheduling, retries, alerting; CI/CD basics for data pipelines.
- Data governance and quality: Validations, profiling, data contracts, lineage/catalog documentation; adherence to privacy/security policies. Collaboration and delivery: Translate stakeholder requirements; produce clear documentation and reproducible code artifacts; manage SLAs.
- Preferred experience in the following:
- Orchestration: Airflow/Prefect, event-driven and batch scheduling; SCD patterns, CDC/streaming (Kafka/EventHub).
- Platform components: Delta Live Tables, Feature Stores, Databricks SQL/Unity governance; Terraform/Bicep for infra-as-code.
- Integration: APIs, semi-structured/unstructured data (JSON/Avro), file ingestion at scale; federated/virtualized query layers.
- Domain: Utility/energy datasets (AMI, SCADA), compliance/governance in regulated environments.
Compensation Information:
$84,180 - $105,210 (min. - mid.)
Data Scientist III
- Bachelor’s Degree in Computer Science, Engineering, or related discipline + 5 years experience or master’s degree in computer science, Engineering, or related discipline + 3 years experience in data engineering (ETL/ELT, cloud warehousing, analytics) with advanced expertise in Databricks, Informatica, Python, SQL, and cloud data platforms.
- Must have strong background in cloud data architecture, governance, performance optimization, and technical leadership/communication.
- Must have experience in the following:
- Languages and platforms: Advanced Python and SQL; Databricks (Spark, jobs, Delta), Informatica (ETL/ELT).
- Cloud architecture: Azure/AWS/GCP services for storage/compute/IAM/networking; cost-aware, secure designs.
- Data warehousing: Dimensional/star modeling, Delta/Parquet, medallion architecture, performance tuning, partitioning/z-ordering.
- Engineering excellence : CI/CD for data pipelines, environment management (conda/poetry), testing (unit/integration/data tests), observability (logging/metrics/alerts).
- Data governance and quality: Unity Catalog (or equivalent), lineage, data contracts, DQ checks, privacy/security controls.
- Leadership and collaboration : Stakeholder management, specification writing, mentoring, documentation of standards and runbooks.
- Preferred experience in the following:
- Orchestration: Airflow/Prefect, event-driven and batch scheduling; SCD patterns, CDC/streaming (Kafka/EventHub).
- Platform components: Delta Live Tables, Feature Stores, Databricks SQL/Unity governance; Terraform/Bicep for infra-as-code.
- Integration: APIs, semi-structured/unstructured data (JSON/Avro), file ingestion at scale; federated/virtualized query layers.
- Domain: Utility/energy datasets (AMI, SCADA), compliance/governance in regulated environments.
Compensation Information:
$104,620 - $130,770 (min. - mid.)
