{"id":1842182,"url":"https://alion.io/job/livefront-data-engineer-databricks","title":"Senior Data Engineer (Databricks)","company":{"id":2293103,"name":"Livefront","domain":"livefront.com","url":"https://alion.io/company/livefront","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"B","score":75,"open_postings":8,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-06T05:45:30Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"posting_text","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["PE"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":58000,"max_usd":147000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1839},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"Delta Lake","optional":false},{"name":"Feature Store","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"GitHub","optional":true},{"name":"Java","optional":true},{"name":"LLMOps","optional":true},{"name":"RAG","optional":true},{"name":"Scala","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-07-10T18:53:21Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-07T00:34:49Z","board_verified":true,"closed_at":null,"days_open":88,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":88},"description":"At Livefront, we help companies design and build world-class digital products that command attention and inspire joy. We’ve helped household names like CVS, Samsung, General Mills, and Optum create experiences that have reached millions of people, and startups like HomeSpotter and Credly build entirely new businesses that challenge their industries’ status quo.\nWe're looking for an outstanding Senior 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. This is a senior-level position with the opportunity to work remotely in Peru.\nWho you are\nYou 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.\nYou 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 - and you're as comfortable in a product design conversation as you are building a DLT pipeline.\nYou 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.\nYou have an interest in and a curiosity about data platforms and the latest advances in data technology.\nWhat you will be doing\nDesign 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.\nArchitect and implement Lakehouse solutions on Databricks - medallion architecture, Delta Lake, Unity Catalog - tailored to the client's analytics, AI, and application needs.\nBuild and maintain Databricks transformation layers - DLT pipelines, PySpark notebooks, and dbt - with data quality constraints and SLAs baked in.\nDesign 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.\nCollaborate with product and backend engineers to design data models, APIs, and application data contracts - ensuring the platform serves the product, not just the warehouse.\nConsult with clients to understand their data challenges, develop data strategies, and implement sustainable solutions.\nAdapt your approach based on project needs - sometimes leading data architecture discussions with clients, other times supporting internal teams with specialized data expertise.\nWork within multi-cloud environments - primarily AWS and Azure - anchoring data platform recommendations around Databricks where it fits the client's architecture and goals.\nChampion data governance through Unity Catalog - access control, lineage, data quality policies, and compliance - as a first-class part of every engagement, not an afterthought.\nDesign data-to-application architectures - including Lakebase-backed services and Databricks Apps - that connect governed data to AI workflows, digital products, and user-facing experiences.\nHelp build Livefront's Databricks practice - contributing to accelerators, internal enablement, certification goals, and Databricks partner go-to-market materials alongside delivery work.\nWhy you should apply\nYou want to work with passionate and talented people who are always looking for ways to make things better.\nYou desire a work environment where respect, mutual trust, and egoless collaboration are paramount.\nYou want colleagues who take their work seriously but not themselves, and who know how to let loose and have a good time.\nYou like being part of a team with a reputation for excellence that gives back to the community by educating, mentoring, and sponsoring.\nYou want to work on products and accounts that have outsized impact and reach.\nYou believe in sweating the details, giving a damn about quality, and taking pride in going the extra mile.\nYou 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.\nWhat you bring to the table\n5-8+ years of data engineering experience with at least 4 years in production Databricks environments, preferably in a consulting or client delivery context.\nSolid working knowledge of AWS and Azure cloud services relevant to Databricks deployments - storage, networking, IAM, and compute - with GCP familiarity a plus.\nDeep, 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.\nProven experience designing Lakehouse architectures - medallion patterns, Delta Lake table design, partitioning, Z-ordering, and query optimization - at production scale.\nHands-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.\nStrong proficiency in SQL and Python, with the ability to write clean, performant, and maintainable code.\nUnderstanding of data modeling, schema design, and query optimization.\nExcellent communication skills with the ability to explain complex data concepts to both technical and non-technical stakeholders.\nStrong problem-solving skills with the ability to navigate ambiguous requirements and deliver pragmatic solutions.\nAbove-average discipline and personal organization skills.\nObvious comfort with critique and peer review in the context of an iterative development process.\nA demonstrated hunger for personal and professional growth.\nA self-evident love and care for the craft of data engineering.\nBonus points if you…\nHave worked with real-time streaming technologies (Kafka, Kinesis, etc.).\nHave hands-on experience with alternative cloud data platforms - useful context for migrations and competitive assessments, though Databricks is our primary platform focus.\nHave experience in healthcare or fintech domains.\nHave hands-on experience with MLOps or LLMOps on Databricks - MLflow experiment tracking, model registry, Model Serving endpoints, or Vector Search for RAG pipelines.\nHave experience with Java, Go, or Scala.\nHave strong illustration skills for technical diagramming and data architecture documentation.\nSpeak, write, and/or educate publicly about data engineering topics.\nHave contributed to open-source data projects.\nHold 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.\nHave experience with Databricks Apps, or Lakebase - early familiarity with where the Databricks platform is heading is a strong differentiator.\nWhat to expect\nWhen 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).\nOur 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.\nAdditional information\nWe 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. 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