Solution Architect

Lovelytics is a Databricks-focused data and AI consulting firm specializing in artificial intelligence, data, and analytics solutions. Since partnering with Databricks in 2019, Lovelytics has experienced exponential growth, growing from 50 people to over 340 over the past 3 years. Lovelytics is a trusted partner for many of the most high-profile enterprise clients in Media & Entertainment, Manufacturing, Retail & CPG, Healthcare & Life Sciences, and Financial Services.

We're looking for a Solution Architect to lead the design of scalable, end-to-end data solutions for clients across industries. This role bridges strategy, architecture, and delivery—working closely with sales, engineers, and business stakeholders.

You’ll play a critical part in presales conversations, define solution architectures, and guide the successful delivery of data platforms, pipelines, analytics environments, and ML capabilities. This is a client-facing role for a hands-on architect with a broad technical skill set and excellent communication abilities.

This role is open to remote candidates in the US and Ontario, Canada

Responsibilies

Design modern data architectures covering ingestion, storage, transformation, analytics, and machine learning

Define scalable cloud-based solutions using tools like Snowflake, Databricks, dbt, Fivetran, Airflow, and others

Guide teams through data warehouse and lakehouse design, ELT/ETL pipelines, and real-time processing solutions

Lead architecture reviews and ensure best practices across performance, security, scalability, and maintainability

Provide hands-on technical leadership during delivery, including mentoring engineers and supporting solution implementation

Partner with sales and account teams to support client discovery, requirements gathering, and scoping

Lead technical aspects of proposals, RFPs, and solution demos

Translate business needs into solution blueprints and recommend appropriate tools and patterns

Strong client-facing and communication skills, with the ability to influence both technical and executive audiences

Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field

8+ years in data engineering, analytics, or architecture roles

Proven experience designing and implementing modern data platforms in cloud environments (AWS, Azure, GCP)

Knowledge across:

Data ingestion (batch & streaming – Kafka, Fivetran, APIs)

Storage & processing (Snowflake, Databricks, Redshift, BigQuery)

Data transformation (dbt, Spark, SQL, Python)

BI/analytics tools (Tableau, Power BI, Looker)

ML workflows and platforms (MLflow, SageMaker, – a plus)

Cloud Infrastructure