Data Engineer - Remote

UnitedHealth Group•Minnetonka, MN

22h•$72,800 - $130,000•Remote

About The Position

This role is responsible for designing, developing, and maintaining scalable and reliable data pipelines that support both batch and real-time analytics within an Azure-based data platform. The position operates as part of a collaborative data engineering team, working closely with fellow data engineers and data science & reporting partners to meet evolving data requirements. The scope of the role includes end-to-end pipeline development using Azure Data Factory, Databricks, PySpark, and streaming technologies; implementation of the Medallion (Bronze/Silver/Gold) architecture; enforcement of data quality, reliability, and performance standards; and adherence to enterprise data governance, security, and documentation practices across the data lifecycle. You’ll enjoy the flexibility to work remotely from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Requirements

3+ years of experience in data engineering role designing and implementing data pipelines and ETL processes. Should have understanding on how to handle incremental data loads and maintain history (CDC – change data capture)
2+ years of experience in SQL for data manipulation and query optimization. Knowledge of Python and Apache Spark (using PySpark) for building data pipelines; ability to write efficient code for batch and streaming data transformations
1+ years of experience using Azure, Databricks or an equivalent cloud-based data platform. Comfortable with managing clusters, using notebooks, and working with Delta Lake or Parquet files. Familiarity with cloud data services and tools for pipeline orchestration is expected
Experience working in a team environment with agile methodologies. Ability to communicate effectively with both technical peers and non-technical stakeholders (explaining data issues in plain language). Should be comfortable using version control systems and participating in collaborative development (code reviews, pair programming when needed)
Familiar with streaming data technologies. This could include Spark Streaming, Kafka, Azure Event Hubs, or similar platforms for real-time data ingestion.
Demonstrated ability to detect and correct data issues – for instance, identifying when a data source has stopped updating, or when an upstream change has altered data format. Experience implementing validation checks or using frameworks to enforce data quality standards

Nice To Haves

Experience with any declarative pipeline frameworks or data workflow management tools (e.g., Databricks Delta Live Tables). This can indicate readiness to adopt advanced tools in our environment
Experience integrating data quality checks into pipelines (such as using assertions or Great Expectations tests) to ensure accuracy and completeness of data. Familiarity with data security practices, encryption, and handling of sensitive data
Familiarity with streaming data handling (even if assisting, should understand basics of Spark Streaming or message queue systems) is expected
Demonstrated skill in performance tuning for Spark or SQL queries. For example, experience in partitioning strategies, caching, or troubleshooting shuffle issues to optimize heavy data workloads

Responsibilities

Design, develop, and maintain robust pipelines to ingest data from various sources (both streaming and batch) into the analytics environment using Azure Data Factory and PySpark via Databricks. Set up real-time data ingestion using tools like Spark Structured Streaming and batch ETL jobs for periodic data loads. Ensure these pipelines are scalable, efficient, and fault-tolerant to handle growing data volumes and velocity.
Utilize the Medallion (Bronze/Silver/Gold) architecture principles to organize data processing stages. Establish raw data capture (bronze), perform cleansing and transformations (silver), and curate refined datasets for analysis and machine learning (gold). Apply best practices in each layer, such as schema enforcement and checkpointing for streaming data.
Optimize Spark jobs by tuning configurations, improving query logic, and managing resources to achieve high throughput and low latency. Address bottlenecks in streaming pipelines (e.g., by scaling clusters or tweaking batch intervals) and ensure timely data delivery. Optimize job scheduling and cluster utilization to balance timely data delivery with cost-effectiveness.
Build and maintain data pipelines with an emphasis on data cleaning steps. Integrate data from various sources (APIs, databases, file feeds, IoT streams, etc.) into the data platform, writing transformations that handle anomalies (e.g., missing or corrupt values) and standardize datasets. Collaborate with the other data engineer to share responsibility across different pipelines or sources, ensuring redundancy and knowledge transfer.
Implement comprehensive data validation rules and checks within pipelines. For example, verify schema correctness, check value ranges for sensor or health data, and ensure referential integrity where applicable. Set up automated alerts or logs that flag inconsistent or bad data, enabling quick intervention. Over time, build a library of data quality tests that run as part of the pipeline (for both streaming and batch processes) to catch issues early.
Leverage modern pipeline frameworks and tools to improve development productivity. For example, use Databricks Delta Live Tables or Lakehouse pipelines to declaratively define data flows where applicable. Explore the use of Spark Declarative Lakeflow Pipelines or similar technologies to simplify the orchestration of complex data processes.
Implement monitoring and alerting for pipeline health. Investigate and resolve problems such as data delays, pipeline failures, or data inconsistencies. Use logs, error messages, and analytics to identify root causes (e.g., source system changes, bug in transformation logic) and implement fixes. Work closely with the other data engineer and data science team members to understand data requirements and adjust pipelines accordingly. Document data engineering workflows and ensure proper data governance (security, privacy, access controls) is in place.
Maintain clear documentation of data pipelines, including data source details, transformation logic, and data destination schemas. Ensure that data lineage is tracked so one can trace how data moved and changed through the system. Adhere to data governance policies – for instance, ensure sensitive data is properly masked or encrypted in non-production environments, and that access controls are in place. Work with leadership to periodically review and improve data management practices.