{"id":2408970,"url":"https://alion.io/job/ssidecisions-senior-ai-data-engineer","title":"Senior AI Data Engineer","company":{"id":4417037,"name":"Strategic Systems International","domain":"ssidecisions.com","url":"https://alion.io/company/ssidecisions","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Breezy","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lahore, Pakistan"],"countries":["PK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":31000,"max_usd":79000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2157},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon EKS","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Data Vault","optional":false},{"name":"Delta Lake","optional":false},{"name":"Dimensional Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Google GKE","optional":false},{"name":"gRPC","optional":false},{"name":"IAM","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"OCR","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Weaviate","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"dbt","optional":true},{"name":"Feature Store","optional":true},{"name":"Flink","optional":true},{"name":"Great Expectations","optional":true},{"name":"Hybrid Search","optional":true},{"name":"Java","optional":true},{"name":"MLFlow","optional":true},{"name":"PostgreSQL","optional":true},{"name":"RAG","optional":true},{"name":"Rust","optional":true},{"name":"Scala","optional":true},{"name":"Spark","optional":true},{"name":"TypeScript","optional":true},{"name":"Weights & Biases","optional":true}],"status":"live","first_seen_at":"2026-10-02T07:28:42Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-13T19:16:26Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":11},"description":"Senior AI Data Engineer\nJob Summary\nWe are seeking a Senior AI Data Engineer to design and operate the data foundation that our AI systems depend on. This role owns the movement, modeling, and quality of data from source systems through the warehouse and into the retrieval and feature layers that power LLM pipelines, agentic workflows, and analytical products.\nThe ideal candidate is a rigorous software engineer first and a data specialist second: someone who models a warehouse deliberately, writes production Python that other engineers can extend, and treats pipelines as versioned, tested, observable software rather than scripts.\nThis role partners closely with the AI/ML Data Scientist, who owns model behavior and retrieval strategy.\nThe boundary: you own the pipeline, the schema, and the guarantees; they own the algorithm, the prompt, and the evaluation.\n\nKey Responsibilities\nData Warehousing & Dimensional Modeling\nDesign and evolve dimensional models using Kimball methodology - star schemas, conformed dimensions, an enterprise bus matrix, and explicit fact-table grain declarations.\nImplement transaction, periodic snapshot, and accumulate snapshot fact tables as the business process warrants and defend the choice of grain.\nManage slowly changing dimensions (Type 1 / 2 / 3, and hybrid variants) with correct effective-dating, surrogate key strategy, and late-arriving dimension handling.\nModel semi-structured and unstructured sources - documents, transcripts, event streams-into queryable structures without discarding provenance.\nMaintain a governed semantic layer so that both human analysts and agentic consumers resolve the same metric to the same number.\nPipeline & ETL/ELT Engineering\nBuild and operate batch and streaming pipelines with orchestration frameworks such as Airflow, Prefect, or Dagster, including backfill, replay, and idempotent-retry semantics.\nImplement ELT transformation layers with tested, documented, version-controlled SQL.\nOwn data contracts between producing and consuming systems: schema evolution, compatibility rules, and breaking-change procedure.\nInstrument pipelines for observability - freshness, volume, distribution, and schema-drift checks - with alerting that distinguishes a real incident from ordinary variance.\nMaintain end-to-end lineage from source record to warehouse fact to retrieved chunk.\nData Engineering for AI\nBuild embedding generation and refresh pipelines: chunk materialization, embedding jobs, incremental re-embedding on source change, and index lifecycle management.\nOperate vector stores (Pinecone, Weaviate, Chroma, Milvus, pgvector) as production data systems - capacity, index build strategy, upsert/delete correctness, and staleness SLAs.\nBuild extraction pipelines over unstructured sources using OCR, document parsers, and vision-language model outputs, treating extraction confidence as a first-class column.\nImplement the preprocessing and feature pipelines that back model training and inference, with train/serve consistency as a design requirement.\nExpose data to AI services and applications through well-specified FastAPI or gRPC interfaces.\nEngineering Craft\nApply SOLID principles and domain-driven design: bounded contexts that mirror the business domains, ubiquitous language shared with stakeholders, aggregates and repositories that keep domain logic out of transport and persistence layers.\nMaintain meaningful test coverage (unit, contract, and data-quality assertions) and treat an untested pipeline as an unfinished one.\nOwn CI/CD, containerization, and environment promotion for data services.\nContribute to code review, architectural decision records, and internal standards.\nGovernance & Cost\nImplement access control, PII handling, retention, and audit requirements at the data layer.\nManage warehouse and pipeline cost: partitioning, clustering, materialization strategy, and storage tiering.\nCollaboration\nTranslate business problems into data models with product and client stakeholders.\nMentor junior and mid-level data engineers.\n\nRequired Qualifications\n5-10+ years in software engineering or data engineering, with substantial time in production data platform work.\nData warehousing: demonstrable command of Kimball dimensional modeling - not just familiarity with the vocabulary, but the judgment to choose a grain, resolve a many-to-many relationship, and know when to denormalize. Working knowledge of alternative approaches (Data Vault, One Big Table, Inman) and the tradeoffs against Kimball.\nSQL: expert-level - window functions, CTEs, query plan reading, and performance tuning on a columnar warehouse.\nPython: expert-level, production-grade - typing, packaging, dependency management, testing.\nDesign: SOLID and domain-driven design applied in real systems; with examples you can walk through.\nOrchestration: Airflow, Prefect, Dagster, or equivalent, in production.\nCloud: expert-level on AWS, Azure, or GCP - storage, compute, IAM, networking, and cost management.\nPlatform: containerization, Kubernetes (EKS/AKS/GKE), and CI/CD.\nExperience with Lakehouse table formats (Iceberg, Delta Lake, Hudi) and their maintenance characteristics: compaction, snapshot expiry, schema and partition evolution.\nPreferred Qualifications\nExperience building the data layer beneath production RAG systems, including hybrid search infrastructure and index freshness guarantees.\nStreaming systems: Kafka, Kinesis, Flink, or Spark Structured Streaming.\ndbt or an equivalent transformation and testing framework.\nData quality tooling (Great Expectations, Soda, or similar) and catalog/lineage platforms.\nFamiliarity with the model-facing side of the stack - MLflow, Weights & Biases, feature stores - sufficient to collaborate credibly with data scientists.\nWorking knowledge of a second language: TypeScript, Java, Go, Scala, or Rust.\nExperience with AI security, governance, and compliance frameworks.\nOpen-source contributions to data or AI infrastructure projects.","description_format":"text","description_chars":5975,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-13T01:34:48Z"}],"visa":[],"liveness":{"score":82,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.823,"p_room":1,"age_days":10,"expected_fill_days":37,"reasons":["conf:1","win:early"],"computed_at":"2026-10-13T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/ssidecisions-senior-ai-data-engineer","json_url":"https://alion.io/job/ssidecisions-senior-ai-data-engineer.json","meta":{"generated_at":"2026-10-13T20:01:00Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":30734,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-14T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4417037},"rest":"https://alion.io/mcp/rest/get_company?id=4417037"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fssidecisions-senior-ai-data-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fssidecisions-senior-ai-data-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fssidecisions-senior-ai-data-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/ssidecisions-senior-ai-data-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fssidecisions-senior-ai-data-engineer"}]}