Role Description We're looking for an outstanding Databricks Data Engineer to join our growing data practice — someone who builds the data and AI foundations that digital products and intelligent experiences run on. Who you are: - You are a Databricks-focused Data Engineer who understands that great data platforms are only as valuable as the products, AI workflows, and experiences they enable. - You bring deep, production-grade expertise across the Databricks platform and know how to connect platform capabilities to real business outcomes. - You thrive in ambiguity and can quickly assess a client's data landscape to recommend and implement the right solutions. - You understand that in consulting, your Databricks depth is most valuable when it connects platform capabilities to the products and experiences clients actually use. - You're as comfortable in a product design conversation as you are building a DLT pipeline. - You excel at translating complex data challenges into clear technical requirements and can confidently navigate conversations with everyone from data scientists to executives. - Your engineering principles are mature and grounded in real-world experience across various industries and scales. - You have an interest in and a curiosity about data platforms and the latest advances in data technology. What you will be doing: - Design and build production data pipelines using Lakeflow Declarative Pipelines, Autoloader, and Structured Streaming, with end-to-end ownership of ingestion, transformation, data quality expectations, and CI/CD deployment via Declarative Automation Bundles. - Architect and implement Lakehouse solutions on Databricks — medallion architecture, Delta Lake, Unity Catalog — tailored to the client's analytics, AI, and application needs. - Build and maintain Databricks transformation layers — DLT pipelines, PySpark notebooks, and dbt — with data quality constraints and SLAs baked in. - Design and maintain the data and AI foundations — Unity Catalog, Feature Store, MLflow, and Model Serving — that power production ML, agent workflows, and AI-enabled digital products. - Collaborate with product and backend engineers to design data models, APIs, and application data contracts — ensuring the platform serves the product, not just the warehouse. - Consult with clients to understand their data challenges, develop data strategies, and implement sustainable solutions. - Adapt your approach based on project needs — sometimes leading data architecture discussions with clients, other times supporting internal teams with specialized data expertise. - Work within multi-cloud environments — primarily AWS and Azure — anchoring data platform recommendations around Databricks where it fits the client's architecture and goals. - Champion data governance through Unity Catalog — access control, lineage, data quality policies, and compliance — as a first-class part of every engagement, not an afterthought. - Design data-to-application architectures — including Lakebase-backed services and Databricks Apps — that connect governed data to AI workflows, digital products, and user-facing experiences. - Help build Livefront's Databricks practice — contributing to accelerators, internal enablement, certification goals, and Databricks partner go-to-market materials alongside delivery work. Qualifications - 3-5 years of data engineering experience with at least 2 years in production Databricks environments, preferably in a consulting or client delivery context. - Solid working knowledge of AWS and Azure cloud services relevant to Databricks deployments — storage, networking, IAM, and compute — with GCP familiarity a plus. - Deep, production-grade Databricks expertise: Lakeflow Declarative Pipelines, Autoloader, Structured Streaming, Lakeflow Jobs, Unity Catalog (including fine-grained access control and lineage) — demonstrated through shipped production workloads, not prototypes. - Proven experience designing Lakehouse architectures — medallion patterns, Delta Lake table design, partitioning, Z-ordering, and query optimization — at production scale. - Hands-on experience with data pipeline testing, observability, and CI/CD for data — including unit testing, data quality frameworks, and version-controlled deployments via Git and Declarative Automation Bundles. - Strong proficiency in SQL and Python, with the ability to write clean, performant, and maintainable code. - Understanding of data modeling, schema design, and query optimization. - Excellent communication skills with the ability to explain complex data concepts to both technical and non-technical stakeholders. - Strong problem-solving skills with the ability to navigate ambiguous requirements and deliver pragmatic solutions. - Above-average discipline and personal organization skills. - Obvious comfort with critique and peer review in the context of an iterative development process. - A demonstrated hunger for personal and professional growth. - A self-evident love and care for the craft of data engineering. Requirements - Bonus points if you have worked with real-time streaming technologies (Kafka, Kinesis, etc.). - Have hands-on experience with alternative cloud data platforms — useful context for migrations and competitive assessments, though Databricks is our primary platform focus. - Have experience in healthcare or fintech domains. - Have hands-on experience with MLOps or LLMOps on Databricks — MLflow experiment tracking, model registry, Model Serving endpoints, or Vector Search for RAG pipelines. - Have experience with Java, Go, or Scala. - Have strong illustration skills for technical diagramming and data architecture documentation. - Speak, write, and/or educate publicly about data engineering topics. - Have contributed to open-source data projects. - Hold or are actively pursuing a Databricks certification (Data Engineer Associate or Professional, or Apache Spark Developer) — we treat these as meaningful signals of platform depth, and they directly support our Databricks partner growth goals. - Have experience with Databricks Apps, or Lakebase — early familiarity with where the Databricks platform is heading is a strong differentiator. Benefits - You want to work with passionate and talented people who are always looking for ways to make things better. - You desire a work environment where respect, mutual trust, and egoless collaboration are paramount. - You want colleagues who take their work seriously but not themselves, and who know how to let loose and have a good time. - You like being part of a team with a reputation for excellence that gives back to the community by educating, mentoring, and sponsoring. - You want to work on products and accounts that have outsized impact and reach. - You believe in sweating the details, giving a damn about quality, and taking pride in going the extra mile. - You want to help build a data practice specialization from the ground up — shaping how we go to market with Databricks, what we build as accelerators, and what it means to do this kind of work at a digital product company. What to expect - When applying, please include a short note about yourself, a summary of your work experience, and a link to any public profiles you actively maintain (e.g., GitHub, LinkedIn, etc). - Our hiring process moves quickly and consists of several stages for candidates who capture our attention with their initial submission, sometimes including but not limited to a short preliminary phone interview, a series of video interviews, and a short take-home exercise, which you'll have up to a week to complete. - Compensation is $120,000 - $145,000. Additional information We go out of our way to evaluate all employees and job applicants equally based on merit, competence, and qualifications. We encourage candidates from all backgrounds to apply and consider all qualified applicants. Don't worry, every application will be reviewed by a human.

Role Description We are looking for a Senior Data Engineer to build and evolve the data platform powering our global workforce management ecosystem. You will design, implement, and maintain scalable data pipelines that consolidate data from multiple operational systems, transform it into trusted analytical datasets, and make it available for reporting, product analytics, and business intelligence. You should be comfortable working with modern cloud-native data architectures on AWS, building reliable ETL/ELT pipelines, and designing data models optimized for analytical workloads. This role requires a strong engineering mindset, balancing performance, scalability, data quality, and operational excellence while collaborating closely with software engineers, product teams, analysts, and data scientists. What You Will Own - Design, build, and maintain scalable batch and streaming data pipelines using AWS-native services and distributed processing frameworks - Develop ETL/ELT workflows to ingest, consolidate, sanitize, enrich, and transform data from multiple internal and external systems - Build and optimize AWS Data Lake solutions using Amazon S3, AWS Glue, Amazon Redshift, and Amazon Kinesis Firehose - Design and implement distributed data processing jobs using Apache Spark, AWS Glue, Databricks, or equivalent technologies - Develop orchestration workflows using Apache Airflow (MWAA), AWS Step Functions, or similar workflow orchestration platforms - Design analytical data models including star schemas, snowflake schemas, dimensional models, and optimized reporting datasets - Optimize Redshift performance through distribution strategies, sort keys, partitioning, workload tuning, and query optimization - Build resilient pipelines supporting retries, idempotency, checkpointing, incremental processing, and partial failure recovery - Implement automated data quality validation, schema evolution, lineage tracking, and governance controls - Develop infrastructure and deployment automation using Infrastructure as Code and CI/CD pipelines - Monitor, troubleshoot, and continuously improve the reliability, scalability, and performance of the data platform - Collaborate with analysts, software engineers, data scientists, and product managers to translate business requirements into scalable data solutions - Participate in architecture discussions and contribute technical documentation, standards, and best practices Qualifications - 5+ years of professional experience building production data pipelines and cloud-based data platforms - Strong experience with AWS data services including Amazon Redshift, AWS Glue, Amazon S3, and Amazon Kinesis Firehose - Strong Python programming skills for ETL development, automation, event processing, and scripting - Advanced SQL expertise including query optimization, window functions, analytical queries, versioned migrations, rollback strategies, and warehouse tuning - Experience designing scalable ETL/ELT pipelines for both batch and streaming workloads - Experience with distributed compute and storage using Apache Spark, AWS Glue, Databricks, or similar distributed processing frameworks - Strong understanding of data warehousing concepts including dimensional modeling, star schemas, snowflake schemas, partitioning strategies, and analytical data structures - Experience designing end-to-end data architectures including ingestion, transformation, orchestration, and consumption layers - Experience implementing workflow orchestration using Apache Airflow (MWAA), AWS Step Functions, or equivalent orchestration tools - Understanding of data governance, metadata management, security best practices, IAM, encryption, and regulatory compliance considerations - Experience with Git-based collaborative development workflows, CI/CD pipelines, Infrastructure as Code, deployment approvals, versioned migrations, and safe rollback strategies - Experience monitoring and maintaining production data infrastructure, ensuring high availability, observability, data quality, and operational reliability - Strong communication skills with the ability to explain technical concepts to business stakeholders and collaborate effectively across engineering, analytics, and product teams Nice to Have - Experience with Apache Iceberg, Delta Lake, Apache Hudi, or modern open table formats - Experience with dbt or SQL-based transformation frameworks - Familiarity with Kafka, Amazon MSK, or other streaming platforms - Experience with Lakehouse architectures and modern analytical data platforms - Knowledge of Terraform or AWS CloudFormation - Experience with containerized data workloads using Docker and ECS/EKS - Experience implementing DataOps practices and automated testing for data pipelines - Familiarity with BI platforms such as Tableau, Power BI, Looker, or QuickSight - Experience implementing data catalogs, lineage, and governance solutions - Exposure to machine learning feature pipelines or data science infrastructure Tech Stack - Programming: Python, SQL, PySpark - Data Processing: Apache Spark, AWS Glue, Databricks - Data Storage: Amazon S3, Amazon Redshift, Parquet - Streaming: Amazon Kinesis Firehose, EventBridge - Orchestration: Apache Airflow (MWAA), AWS Step Functions - Data Modeling: Star Schema, Snowflake Schema, Dimensional Modeling - Infrastructure: AWS, IAM, CloudWatch - IaC/CI: Git, GitHub Actions, Terraform, CloudFormation - Observability: CloudWatch, Datadog (or equivalent observability platforms) - Governance: Data Catalog, Metadata Management, Data Lineage

Role Description Design, develop, and maintain scalable ELT pipelines and modern data architectures. - Build and optimize data transformation workflows using dbt and SQL. - Collaborate closely with software engineers and product teams to support data-driven applications. - Develop infrastructure as code using Terraform. - Support web application integrations involving Node.js, TypeScript, GraphQL, and React. - Improve data reliability, monitoring, and operational excellence across the platform. - Participate in production support through an on-call rotation (1 week every 4 weeks) and serve as backup on-call engineer for an additional week every 4 weeks. - Collaborate with US-based engineering teams to troubleshoot production issues and continuously improve platform performance. - Contribute to best practices around data engineering, automation, and cloud architecture. Qualifications - Strong experience building ELT pipelines and modern data engineering solutions. - Advanced experience with dbt and SQL. - Hands-on experience with Terraform. - Experience working with web application technologies, including: Node.js, TypeScript, GraphQL, React. - Strong analytical and problem-solving skills. - Experience working in Agile environments. - Excellent communication skills and ability to collaborate with distributed international teams. - English proficiency sufficient to work daily with US-based engineers. Requirements - Nice to have: Snowflake. - AWS. - Apache Airflow. - Looker. - Python. - GitHub. - Experience supporting production environments and participating in on-call rotations. - Experience designing modern cloud-native data platforms. - English level: Upper-Intermediate. Benefits - International projects. - In-office, hybrid, or remote flexibility. - Medical healthcare. - Recognition program. - Ongoing learning & reimbursement. - Well-being program. - Team events & local benefits. - Sports compensation. - Referral bonuses. - Top-tier equipment provision.

• Design, develop, and maintain scalable ETL/ELT pipelines using Databricks and Python. • Build, test, and support integrations between Databricks, Oracle, and downstream systems. • Develop and maintain high-quality data extracts for internal and external stakeholders. • Own and document inbound and outbound data feeds. • Monitor data pipelines for performance, reliability, and quality; identify and resolve issues. • Design and optimize data models supporting operational, analytical, and financial reporting. • Contribute to continuous improvement of the data platform using industry best practices. • Partner with technical and business stakeholders to gather requirements and deliver solutions. • Support internal applications and ensure business continuity. • Collaborate effectively across engineering and cross-functional teams.