Data Engineer - Quant Trading About Deeter Investments Deeter Investments is a founder‑led proprietary trading firm built around real‑time, data‑driven decision‑making. We prize curiosity, collaboration, and a bias for action. After years of discretionary success, we’re launching a dedicated algorithmic division. Role Description As our first Data Engineer, you’ll own critical datasets end-to-end from ingestion and system architecture to reliability and access. You'll be designing, building, and running the data backbone for our algorithmic team. You’ll work directly with traders and researchers to turn messy external feeds into high-performance, well-structured datasets that guide decisions in research and production. Key Responsibilities - Architect cloud-native batch and streaming ELT for diverse sources; standardize, de-duplicate, document; define schemas and redundancy. - Stand up core platform: storage/lakehouse, orchestration, metadata/catalog, CI/CD, IaC, observability; keep it simple and cost-aware. - Implement data quality checks, anomaly detection; maintain survivorship-bias-free histories and handle corporate actions/entitlements. - Expose clean data via APIs/query layers and shared libs; produce “research-ready” datasets for fast backtests and production. - Partner with quants/DS/SWE to scope, prototype, and productionize new datasets quickly; own incident response and runbooks. - Uphold security and access hygiene (IAM/least-privilege, secrets, audit). Qualifications & Experience - 5+ years building and operating production data pipelines/platforms (or equivalent). - Strong Python and SQL; ideally familiarity with distributed, time-series, or NoSQL databases. - Comfortable on at least one major cloud (AWS/GCP/Azure). - Docker and Terraform (or similar). - Orchestration (e.g. Airflow/Prefect/Dagster), distributed/batch compute (e.g. Spark/Dask/Beam), warehouses/lakes, columnar formats (e.g. Parquet/Delta/Iceberg). - Monitoring/observability (logs/metrics/traces) and cost management. - Proven delivery for quantitative users or ML/DS teams; clear thinking, clean design, pragmatic trade-offs. Nice to Have - Financial/time-series data (corporate actions, vendor entitlements/licensing), alternative data ingestion. - Multimodal ETL (NLP/embeddings, transcription, basic image/video processing). - Dataset/version control and reproducibility (e.g., LakeFS/DVC) and research workflow tooling. Location: Remote Language: English required Employment: Full-time
Social Media Data Analyst About Deeter Investments Deeter Investments is a founder-led proprietary trading firm built around real-time, data-driven decision-making. We prize curiosity, collaboration, and a bias for action. After years of discretionary success, we’re expanding our algorithmic division—and we’re hiring our first Social Media Data Analyst to turn the internet’s noise into tradable signal. Role Description You’ll be the point person for sourcing, cleaning, and interpreting social-media and web-native data (X/Twitter, Reddit, TikTok, YouTube, Discord/Telegram, major news, and niche forums). Your job: identify early moves, sentiment shifts, and “virality” patterns that matter for markets—and get those insights into traders’ hands fast via dashboards, alerts, and research-ready datasets. This is a hands-on role: you’ll pull data from APIs/brokers, structure it, label it, score it, test what actually predicts price/volume, and ship lightweight tools that the team uses daily. Key Responsibilities · Data sourcing & hygiene: Acquire streams from platforms and data brokers; de-duplicate, de-spam, and defeat bots; maintain coverage maps and latency SLAs. · Entity & ticker extraction: Build/maintain pipelines that correctly tag tickers, companies, and themes across slang, emojis, tickers-in-images, and misspellings. · Signal & sentiment: Produce sentiment/stance scores, virality/acceleration metrics, influencer/graph features, and “surprise vs baseline” indicators. · Quality & governance: Track precision/recall of extraction, false-positive rates, freshness, and source reliability; document assumptions and licensing/entitlements. · Collaboration: Work directly with traders, quants, and data engineers to iterate quickly from idea → prototype → production. Qualifications & Experience · 3–6+ years in data analysis or applied analytics (content, social, growth, alt-data, or market intelligence). · Strong Python and SQL ; comfortable wrangling messy text/video-adjacent metadata and large timelines. · Practical NLP toolkit (regex → embeddings/classifiers); able to explain tradeoffs in simple terms. · Experience with social-platform APIs, third-party data brokers, or ethically compliant web ingestion. · Solid statistics for backtests and A/B-style validation; know how to avoid obvious pitfalls (look-ahead bias, survivorship, data leakage). · Communicates crisply: turns complex evidence into one-page briefs and clear “what to do” recommendations. Nice to Have · Markets familiarity (tickers, earnings, filings, corporate actions) and event-study workflows. · Graph/“influence network” features, basic time-series modeling, or anomaly detection. · Experience with columnar data and fast queries (Parquet/Delta/Iceberg; DuckDB/ClickHouse/BigQuery/Snowflake). · Light multimodal experience (ASR/transcription, OCR for screenshots, basic image/video metadata). Example Projects You Might Ship in Month 1–3 · A ticker-tagging & sentiment pipeline that posts concise, source-linked alerts to Slack when social momentum and news flow diverge. · A virality tracker that scores acceleration vs. each ticker’s normal social baseline and shows 1-day/1-week outcomes. · An event study template (CSV in/out + notebook) that any trader can run to test a new social signal in under 10 minutes. What Success Looks Like · Higher signal-to-noise for the desk; earlier heads-up on real catalysts; measurable lift in PnL attribution to social signals. · Clear, reproducible metrics: coverage %, freshness (latency), precision/recall for tagging, and backtest effect sizes with confidence intervals. Location: Remote (Europe time-zone overlap preferred) Language: English Employment: Full-time