ROLE OVERVIEW: This is not a traditional seniority-first data engineering role. The right candidate may come from a senior, lead, staff, analytics engineering, platform engineering, or strong hands-on individual contributor background. What matters most is how they work: they can enter unfamiliar data domains, understand operational context, collaborate with AI tools in tight feedback loops, make practical data decisions, and ship reliable data outcomes quickly and responsibly.

The role sits at the intersection of data engineering, AI-ready data delivery, analytics enablement, and agentic software delivery. We are looking for someone who can build and operate pipelines from MongoDB Atlas and other sources, implement architect-defined canonical models and data contracts, create medallion-style lake / lakehouse datasets, support embeddings and vector pipelines, and use AI agents to scale delivery without losing accountability for correctness, lineage, performance, security, or business meaning.

About Teqfocus: Teqfocus is a Data and AI company that helps enterprises build and scale the Agentic Enterprise — connecting the data foundation, the intelligence layer, and the applications teams run their businesses on into a single, coherent architecture.

As a Salesforce Summit Partner, we work in Healthcare, Life Sciences, Financial Services, Insurance, and Hi-Tech — industries where getting AI right matters and the tolerance for failure is low. Our delivery model is senior-heavy by design. The same team that scopes an engagement ships it to production. There are no junior shadow teams, no sub-contractors brought in when the problem gets hard, and no hand-off to a separate managed services vendor when the project closes.

We run an internal innovation practice that builds the reusable patterns, pre-tested architectures, and industry-specific frameworks that give every Teqfocus client a head start — and give every Teqfocus practitioner work that compounds over time.

We are a diverse, immigrant-entrepreneurial company. We were built on the conviction that the best outcomes come from people who take genuine ownership of what they build — and are trusted to do so.

www.teqfocus.com

WHAT YOU WILL DO Build and operate ingestion, ELT/ETL, and orchestration pipelines that move data from MongoDB Atlas and other operational sources into analytical, lakehouse, and AI-serving layers. Implement layered medallion-style transformations with idempotent, backfillable, and incrementally loaded jobs that can support reporting, analytics, automation, and AI-enabled use cases. Apply deduplication, normalization, validation, reconciliation, and anomaly checks so downstream data is clean, trustworthy, and safe to consume. Implement the canonical data model, schemas, and data contracts defined by the Data Architect, enforcing stable definitions in repositories and pipeline code. Build AI-ready data foundations, including lineage-tracked datasets, metadata, chunking strategy, embeddings-ready content, vector pipelines, retrieval quality, and feature/retrieval-ready outputs for RAG, semantic search, and agentic workloads. Implement real-time and change-data-capture flows from MongoDB using Change Streams / CDC where workloads require fresh data. Modernize legacy or homegrown data flows through incremental, strangler-fig migration patterns that keep production stable while moving toward proven tooling. Instrument pipelines for freshness, volume, schema drift, cost, lineage, quality failures, and downstream impact, with clear alerting, error handling, and runbooks. Use AI coding tools as part of a disciplined data engineering loop: explore, profile, plan, implement, review, test, debug, document, tune, and iterate. Partner with database engineering to extract from and protect the production store, and with ML, AI, application, product, and analytics teams to shape the data they consume. Exercise sound persistence judgment while implementing architecture: land data in the right store, including document / NoSQL, vector, analytical, lakehouse, or warehouse layers, based on the architectural direction. Communicate technical decisions clearly, including trade-offs, assumptions, risks, data quality limitations, governance considerations, and next steps.

REQUIRED QUALIFICATIONS 5+ years of hands-on data engineering experience building and operating production-grade data pipelines or data platforms at scale. Strong programming and data skills with Python and SQL, plus solid software engineering fundamentals including version control, testing, CI/CD, code review, and production support. Hands-on MongoDB at production scale, preferably MongoDB Atlas, including document modeling, aggregation framework, Change Streams / CDC, and extracting from a document store into analytical, lakehouse, vector, or AIserving layers. This is a core requirement, not a nice-to-have. Experience designing ELT/ETL pipelines, transformation frameworks such as dbt or equivalent, and orchestration using Airflow, Dagster, Azure Data Factory, Databricks Workflows, or similar tools. Experience building on cloud-native data platforms and lake / lakehouse / warehouse architectures, including layered bronze, silver, and gold or curated data modeling. Practical understanding of batch, event-driven, and real-time data movement, APIs, async processing, CDC, and integration patterns. Experience implementing data models, schemas, data contracts, data quality checks, reconciliation, lineage, and pipeline observability. Hands-on experience preparing data for AI/ML or analytical consumers, including embeddings / vector pipelines, RAGready datasets, feature-ready datasets, deduplication, normalization, and validation. Familiarity with vector search and embeddings in production, preferably MongoDB Atlas Vector Search or an equivalent vector database/search capability. Demonstrated use of AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, ChatGPT, Codex, Aider, Windsurf, or similar tools for data and pipeline work. Ability to work in existing codebases and brownfield data environments, not only greenfield projects, while modernizing safely and incrementally. Ability to evaluate AI-generated code, SQL, mappings, transformations, tests, and documentation for correctness, maintainability, security, performance, cost, and business fit. Ability to combine engineering judgment with product and data-domain thinking rather than waiting for detailed specifications. Strong communication skills, curiosity, ownership, comfort in complex specialized domains, and a bias toward reliable, usable, governed data outcomes.

AI-NATIVE DATA ENGINEERING EXPECTATIONS Explore unfamiliar MongoDB models, schemas, jobs, pipelines, and codebases with AI while independently verifying actual logic, data behavior, and business meaning. Use AI agents to profile datasets, inspect schemas, propose mapping logic, generate SQL or Python, draft tests, create runbooks, and document data flows. Break ambiguous data requests into practical implementation plans that agents can execute without constant handholding. Generate pipeline, CDC, transformation, or validation code in small, reviewable increments and inspect diffs before accepting changes. Run tests, compare record counts, validate sample outputs, check edge cases, verify freshness and schema behavior, and use failures as feedback for the next iteration. Ask the AI to revise, simplify, optimize, or abandon approaches when the output is incorrect, overcomplicated, unsafe, expensive, or risky. Validate data quality, lineage, retrieval readiness, governance, and business meaning, not just whether a query runs or a pipeline completes. Understand when AI/agent usage is worth the cost based on productivity, quality, speed, risk reduction, token usage, context-window limits, and output quality. Know when not to use AI and remain accountable for final data logic, quality, lineage, security posture, privacy, cost, and downstream impact.

What We offer: Competitive market-aligned hourly compensation Flexible hybrid work environment depending on client needs Opportunity to work on enterprise-scale Salesforce, AI, and cloud transformation initiatives Access to certification support and continuous learning opportunities Exposure to strategic client programs and partner ecosystems including Salesforce and Snowflake Collaborative, fast-moving, and innovation-driven team culture Opportunity for long-term engagement based on performance and business needs

The agentic enterprise is not a future state. It is being built right now, on live production systems, for real clients. If you want to be one of the practitioners building it — this is the right place. Apply Now!!!