Data Analytics Engineer

This onsite role in Maplewood, New Jersey offers a competitive annual compensation between USD 110,000 and 115,000. You will join a telehealth organization focused on serving individuals with intellectual and developmental disabilities (I/DD). StationMD provides 24/7 telemedicine access with board-certified clinicians and specialized psychiatry services, aiming to deliver high quality care while reducing unnecessary medical costs. You’ll be part of a collaborative Data & Analytics team that emphasizes reliable data assets and governance to enable future analytics and operational insights.

Responsibilities

• Design, build, and maintain data ingestion and transformation pipelines across healthcare, operational, contract, patient, roster, and enterprise source systems.
• Support the enterprise analytics platform foundation, including raw, standardized, and curated data layers.
• Create reusable ingestion and transformation patterns using Snowflake, SQL, and related data engineering tools.
• Partner with internal teams and consulting partners to implement metadata-driven pipeline controls, process logging, audit columns, and batch tracking.
• Implement data quality checks to verify completeness, accuracy, timeliness, duplicate handling, and source-to-target reconciliation.
• Help build and maintain control tables, error logging, reject handling, monitoring, and recovery processes for data pipelines.
• Support file-based ingestion patterns, including source file tracking, raw file preservation, archive/quarantine processes, and reprocessing controls.
• Develop analytics-ready data models to support operational, leadership, financial, clinical operations, and self-service analytics.
• Collaborate with business stakeholders to understand data definitions, business rules, source system nuances, and reporting needs.
• Support data governance practices, including data lineage, metadata documentation, access controls, data stewardship, and metric standardization.
• Apply security and privacy best practices for PHI/PII handling, data encryption, role-based access, and auditability.
• Participate in testing, validation, troubleshooting, and production support for data pipelines and analytics datasets.
• Create and maintain technical documentation, data dictionaries, runbooks, and support procedures.
• Use Git or similar version control to manage analytics code, promote changes across environments, and support peer review.
• Collaborate with reporting and analytics users to ensure curated datasets are reliable, understandable, and fit for business consumption.
• Leverage data modeling approaches such as star and dimensional models, slowly changing dimensions, or data vault concepts as appropriate.

Requirements

• Bachelor’s degree in Computer Science, Statistics, Data Science, or a related field; advanced degree preferred.
• 3+ years of experience in data engineering, analytics engineering, BI engineering, data warehousing, or ETL/ELT development.
• Strong SQL skills with experience building, testing, and optimizing data transformations.
• Experience with Snowflake or a comparable cloud data platform such as Azure SQL, Databricks, Redshift, or PostgreSQL.
• Experience designing or supporting ETL/ELT pipelines using batch, incremental, or file-based ingestion patterns.
• Understanding of modern data platform concepts including raw/bronze, standardized/silver, curated/gold, dimensional modeling, and analytics-ready datasets.
• Experience implementing data quality checks, reconciliation logic, audit columns, and error handling.
• Ability to troubleshoot production data issues, identify root causes, and support pipeline recovery.
• Experience documenting data pipelines, data definitions, business rules, and technical support procedures.
• Experience using Git or similar version control tools.
• Strong communication skills with the ability to work with technical teams, business stakeholders, and external partners.

Technologies

• Snowflake
• SQL
• Azure SQL
• Databricks
• Redshift
• PostgreSQL
• Git
• dbt
• Airflow
• Azure Data Factory
• Fivetran
• Matillion
• Informatica
• SSIS
• Python
• Snowpark
• Qlik
• Power BI
• Tableau
• Sigma
• Salesforce
• Salesforce Health Cloud
• Salesforce Data Cloud