Navitas Business Consulting

• Design, build, and maintain scalable ELT/ETL pipelines using tools such as Apache Spark, dbt, Airflow, Prefect, or Dagster.
• Architect and manage data lake and data warehouse solutions on cloud platforms (Snowflake, Big Query, Databricks, Redshift).
• Implement real-time and streaming data pipelines using Apache Kafka, AWS Kinesis, or Azure Event Hubs.
• Ensure data quality, lineage, and observability through automated validation frameworks and metadata management (dbt tests, Great Expectations, Monte Carlo).

• Build and maintain Feature Stores (Feast, Tecton, or Databricks Feature Store) to serve consistent, reusable features for ML models.
• Construct and curate high-quality training, validation, and evaluation datasets for LLMs and traditional ML models.
• Support MLOps workflows by integrating data pipelines with model training, experiment tracking (MLflow, W&B), and model registries.
• Manage vector databases and embedding pipelines for AI-powered search and RAG applications (Pinecone, pgvector, Weaviate).
• Implement data versioning and governance practices aligned with AI compliance requirements.

• Develop and maintain semantic layers, data models, and BI-ready data marts to support dashboards and self-service analytics.
• Build and optimize analytical views, materialized models, and aggregation layers in dbt or equivalent modeling frameworks.
• Partner with analysts and stakeholders to translate business questions into reliable, performant data models.
• Integrate BI platforms (Power BI, Tableau, Looker, or Metabase) with data warehouse sources for real-time and scheduled reporting.
• Platform & Infrastructure
• Manage cloud data infrastructure using Infrastructure-as-Code tools (Terraform, Pulumi) and modern DataOps practices.
• Monitor pipeline health, data freshness, and cost efficiency; proactively identify and resolve bottlenecks.
• Implement role-based access control (RBAC), data masking, and PII governance frameworks to ensure regulatory compliance.
• Contribute to the development and adoption of internal data standards, best practices, and documentation.

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Statistics, or a related field; master’s degree preferred.
• 4+ years of experience in a data engineering role, with at least 1–2 years focused on AI/ML data infrastructure or BI environments.
• Proven track record delivering enterprise-scale data pipelines and BI solutions in production.

• Advanced proficiency in SQL; expertise in Python for data engineering (PySpark, Pandas, SQL Alchemy).
• Hands-on experience with a modern cloud data warehouse (Snowflake, Big Query, or Databricks) and dbt for data transformation.
• Experience with orchestration frameworks such as Apache Airflow, Prefect, or Dagster.
• Working knowledge of distributed processing frameworks: Apache Spark, Flink, or Beam.
• Familiarity with ML lifecycle tooling: MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
• Experience with at least one major cloud provider (AWS, Azure, or GCP) and its data-native services.

• Experience building and maintaining vector stores or embedding pipelines for generative AI applications.
• Exposure to LLM fine-tuning datasets: data curation, deduplication, RLHF data pipelines.
• Knowledge of data mesh principles and decentralized data ownership models.
• Certifications: AWS Data Analytics Specialty, GCP Professional Data Engineer, Databricks Certified Associate, or dbt Analytics Engineer.

• Strong analytical and debugging mindset with a passion for data quality and reliability.
• Ability to balance speed and scalability when designing data systems under competing priorities.
• Excellent collaboration and communication skills to partner with data scientists, analysts, and business stakeholders.
• Detail-oriented approach to documentation, version control (Git), and code reviews.

Equal Employer/Veterans/Disabled

Navitas Business Consulting is an affirmative action and equal opportunity employer. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact Navitas Human Resources.

Navitas is an equal opportunity employer. We provide employment and opportunities for advancement, compensation, training, and growth according to individual merit, without regard to race, color, religion, sex (including pregnancy), national origin, sexual orientation, gender identity or expression, marital status, age, genetic information, disability, veteran-status veteran or military status, or any other characteristic protected under applicable Federal, state, or local law. Our goal is for each staff member to have the opportunity to grow to the limits of their abilities and to achieve personal and organizational objectives. We will support positive programs for equal treatment of all staff and full utilization of all qualified employees at all levels within Navitas.